MICCAI 2024 Papers with AI Summaries
International Conference on Medical Image Computing and Computer-Assisted Intervention · 856 papers
→ MICCAI 2024 papers with code (487)
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3D Spine Shape Estimation from Single 2D DXA
Bourigault, Emmanuelle (University of Oxford), Zisserman, Andrew (University of Oxford)
RestorationSegmentationConvolutional Neural NetworkTransformerImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: Using a single DXA X-ray image to reconstruct the three-dimensional shape of the spine by predicting the corresponding sagittal plane projection.
3D Vessel Graph Generation Using Denoising Diffusion
Prabhakar, Chinmay (University of Zurich), Menze, Bjoern (University of Zurich)
GenerationData SynthesisGraph Neural NetworkTransformerDiffusion modelGraphBiomedical Data
🎯 What it does: A two-stage generative model based on denoising diffusion is proposed, which first generates the coordinates of vascular nodes and then generates the connectivity between nodes, resulting in a three-dimensional vascular network diagram.
3D-SAutoMed: Automatic Segment Anything Model for 3D Medical Image Segmentation from Local-Global Perspective
Liang, Junjie (Northeastern University), Zaiane, Osmar R. (Amii)
SegmentationTransformerSupervised Fine-TuningBiomedical DataMagnetic Resonance Imaging
🎯 What it does: The 3D-SAutoMed framework is proposed to achieve automation of SAM in 3D medical image segmentation and 3D awareness.
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
Gu, Yi (OMRON SINIC X Corporation), Sato, Yoshinobu (Nara Institute of Science and Technology)
SegmentationDepth EstimationConvolutional Neural NetworkAuto EncoderImagePoint CloudComputed Tomography
🎯 What it does: The 3DDX method is proposed, which completes the full 3D bone surface reconstruction by simultaneously predicting the depth maps of the anterior and posterior surfaces of the bone from a single standard geometric X-ray image.
3DGPS: A 3D Differentiable-Gaussian-based Planning Strategy for Liver Tumor Cryoablation
Wang, Ce (Chinese Academy of Sciences), Zhou, Xiang (Chinese Academy of Medical Sciences and Peking Union Medical College)
OptimizationGaussian SplattingBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A multi-ellipse model based on differentiable Gaussian (3DGPS) is proposed to achieve automatic planning and optimization of liver tumor cryoablation.
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
Fu, Xueming (University of Science and Technology of China), Zhou, S. Kevin (Hohai University)
SegmentationGenerationOptimizationGaussian SplattingImageBiomedical DataComputed Tomography
🎯 What it does: This paper proposes the use of 3D Gaussian representation to reconstruct the three-dimensional structure of coronary arteries from extremely sparse 2D X-ray views.
3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks
Li, Xiaoshuang (Shanghai Jiao Tong University), Kim, Jinman (University of Sydney)
RestorationSegmentationConvolutional Neural NetworkGenerative Adversarial NetworkImageComputed Tomography
🎯 What it does: A progressive hybrid MLP-CNN pyramid network, 3DPX, was designed and implemented to reconstruct 3D structures from a single panoramic X-ray (PX) image of the oral cavity, and to be used for subsequent angle mismatch classification tasks.
7T MRI Synthesization from 3T Acquisitions
Cui, Qiming (University of California San Francisco), Abbasi-Asl, Reza (University of California San Francisco)
Image TranslationSegmentationData SynthesisConvolutional Neural NetworkGenerative Adversarial NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: Developed and evaluated various V-Net-based deep learning models to synthesize 7T MRI from 3T MRI to improve image quality and lesion visualization.
A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation
Zheng, Zhou (Nagoya University), Mori, Kensaku (Aichi Institute of Technology)
SegmentationConvolutional Neural NetworkAuto EncoderImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A weakly supervised laparoscopic image segmentation method based on a Bayesian framework is proposed, which generates high-quality pseudo-labels using sparse annotations and trains the segmentation model, while also quantifying prediction uncertainty.
A Clinical-oriented Lightweight Network for High-resolution Medical Image Enhancement
Wang, Yaqi (Northeastern University), Zaiane, Osmar R. (Amii)
RestorationKnowledge DistillationConvolutional Neural NetworkImageMagnetic Resonance Imaging
🎯 What it does: A lightweight medical image enhancement network CHLNet is proposed, which can simultaneously improve overall image quality and local pathological details at high resolution.
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Hou, Qingshan (Northeastern University), Zaiane, Osmar R. (Amii)
ClassificationRecognitionConvolutional Neural NetworkContrastive LearningImageMagnetic Resonance ImagingComputed Tomography
🎯 What it does: This paper proposes a clinical-oriented multi-layer contrastive learning framework, CoMCL, for extracting lesion features from low-quality medical images and achieving disease diagnosis.
A Curvature-Guided Coarse-to-Fine Framework for Enhanced Whole Brain Segmentation
Zhao, Fenqiang (DAMO Academy, Alibaba Group), Zhang, Ling (DAMO Academy, Alibaba Group)
SegmentationConvolutional Neural NetworkBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A curvature-based coarse-to-fine hierarchical framework is proposed to improve the accuracy of whole brain segmentation.
A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors
Lee, Sangyoon (University of Minnesota), Bolan, Patrick J.
SegmentationOptimizationConvolutional Neural NetworkSupervised Fine-TuningImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A 3D convolutional neural network was designed and trained to automatically predict the position, size, and rotation angle of a single voxel MRS acquisition voxel in brain tumors, achieving automatic voxel localization.
A Domain Adaption Approach for EEG-based Automated Seizure Classification with Temporal-Spatial-Spectral Attention
Fan, Xiaoya (Dalian University of Technology), Wang, Zhong (Dalian University of Technology)
ClassificationDomain AdaptationConvolutional Neural NetworkTime SeriesBiomedical Data
🎯 What it does: This study proposes a classification model for EEG seizure types based on domain adaptation and a spatiotemporal frequency spectrum attention mechanism.
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts
Zhang, Xinru (Beijing Institute of Technology), Bai, Wenjia (Imperial College London)
SegmentationConvolutional Neural NetworkMixture of ExpertsImageMultimodalityMagnetic Resonance Imaging
🎯 What it does: The MoME foundational model is proposed to achieve multi-modal brain lesion segmentation.
A framework for assessing joint human-AI systems based on uncertainty estimation
Konuk, Emir (KTH), Smith, Kevin (KTH Royal Institute of Technology)
ClassificationSegmentationImageBiomedical DataUltrasoundBenchmark
🎯 What it does: A framework for evaluating joint human-AI systems is proposed and validated, and a benchmark evaluation of various uncertainty estimation methods is conducted in the task of diagnosing ovarian tumor ultrasound images.
A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer’s Disease Diagnosis
Ou, Zaixin (ShanghaiTech University), Shen, Dinggang (Shanghai United Imaging Intelligence Co., Ltd.)
ClassificationGraph Neural NetworkMultimodalityBiomedical DataMagnetic Resonance ImagingPositron Emission TomographyAlzheimer's Disease
🎯 What it does: This paper proposes a graph embedding latent space learning and clustering framework (Graph-SLC) for multi-class diagnosis of Alzheimer's disease (AD) in the presence of missing multimodal PET data.
A Hybrid CNN-Transformer Feature Pyramid Network for Granular Abdominal Aortic Calcification Detection from DXA Images
Ilyas, Zaid (Edith Cowan University), Gilani, Syed Zulqarnain (University of Western Australia)
ClassificationObject DetectionConvolutional Neural NetworkTransformerImage
🎯 What it does: A CNN-Transformer model called Hybrid-FPN-AACNet based on Hybrid FPN is proposed, which achieves fine-grained scoring regression of abdominal aortic calcification in DXA VFA images (AAC-24 scores for segments L1-L4) through Dual Resolution Self-Attention (DRSA) and Efficient Feature Fusion Module (EFFM);
A Hyperreflective Foci Segmentation Network for OCT Images with Multi-dimensional Semantic Enhancement
Wang, Xingguo (Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences), Zhao, Yitian (Ningbo Institute of Materials Technology and Engineering)
SegmentationConvolutional Neural NetworkImageBiomedical DataUltrasound
🎯 What it does: A multi-dimensional semantic enhancement network based on attention, MUSE-Net, is proposed for precise segmentation of high-reflective points (HRF) in OCT images.
A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability
Rehman, Abdul (Information Technology University), Sultani, Waqas (Information Technology University)
Object DetectionDomain AdaptationExplainability and InterpretabilityConvolutional Neural NetworkSupervised Fine-TuningImageBiomedical Data
🎯 What it does: Developed a multi-cost microscope, multi-resolution, and multi-camera dataset for leukocyte detection and morphological attribute data called LeukemiaAttri, and proposed an interpretable attribute detector called AttriDet;
A Multi-Information Dual-Layer Cross-Attention Model for Esophageal Fistula Prognosis
Zhang, Jianqiao (La Trobe University), Cui, Hui (University Of Sydney)
Convolutional Neural NetworkTransformerContrastive LearningImageMultimodalityTabularBiomedical DataComputed TomographyElectronic Health Records
🎯 What it does: This paper proposes a multi-modal dual-layer cross-attention (MDC) model that utilizes CT images and clinical tabular data to jointly predict the risk of post-radiotherapy esophageal fistula (EF);
A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smoking
Xia, Wenyao (Western University), Chen, Elvis C. S. (Western University)
Image TranslationRestorationConvolutional Neural NetworkTransformerGenerative Adversarial NetworkOptical FlowImageVideoBenchmark
🎯 What it does: A real paired dataset of surgical smoke and smoke-free images was constructed for the first time, and existing smoke removal algorithms were evaluated using this dataset.
A New Cine-MRI Segmentation Method of Tongue Dorsum for Postoperative Swallowing Function Analysis
Sun, Minghao (Nanjing University of Posts and Telecommunications), Yu, Han (Nanjing University of Posts and Telecommunications)
SegmentationConvolutional Neural NetworkTransformerImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A tongue dorsum segmentation network based on convolution + temporal Transformer is proposed, and quantitative analysis of swallowing function is achieved by automatically extracting tongue dorsum feature points.
A New Dataset and Baseline Model for Rectal Cancer Risk Assessment in Endoscopic Ultrasound Videos
Zhang, Jiansong (Shenzhen University), Shen, Linlin (Shenzhen University)
ClassificationRecognitionTransformerVideoBiomedical DataUltrasoundBenchmark
🎯 What it does: The first endoscopic ultrasound video dataset containing 207 cases of rectal cancer patients and 5 staging labels was constructed, and a Transformer-based RCVA-Net model was proposed for video-level risk assessment.
A New Non-Invasive AI-Based Diagnostic System for Automated Diagnosis of Acute Renal Rejection in Kidney Transplantation: Analysis of ADC Maps Extracted from Matched 3D Iso-Regions of the Transplanted Kidney
Abdelhalim, Ibrahim (University of Louisville), El-Baz, Ayman (University of Louisville)
ClassificationSegmentationTransformerBiomedical DataMagnetic Resonance Imaging
🎯 What it does: An non-invasive automatic diagnostic system based on DW-MRI is proposed for detecting acute rejection of kidney transplants at different Tesla levels (1.5T, 3T).
A New Perspective to Boost Performance Fairness For Medical Federated Learning
Yan, Yunlu (Hong Kong University of Science and Technology (Guangzhou)), Feng, Chun-Mei (Agency for Science, Technology and Research)
SegmentationFederated LearningContrastive LearningImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: The Fed-LWR method is proposed to address the unfairness caused by feature shift in medical federated learning through hierarchical weight re-aggregation.
A Novel Adaptive Hypergraph Neural Network for Enhancing Medical Image Segmentation
Chai, Shurong (Ritsumeikan University), Chen, Yen-Wei (Ritsumeikan University)
SegmentationGraph Neural NetworkImageBiomedical DataMagnetic Resonance ImagingComputed Tomography
🎯 What it does: A medical image segmentation framework based on adaptive hypergraphs is proposed, integrating hypergraph construction and convolutional hypergraph techniques to enhance segmentation accuracy.
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features
Islam, Saahil (Friedrich-Alexander-Universität), Ghesu, Florin C. (Siemens Healthineers)
Object TrackingTransformerContrastive LearningVideoBiomedical DataComputed Tomography
🎯 What it does: This paper proposes a real-time tracking framework HiFT based on self-supervised learning, which uses supplementary vesselness and historical feature guidance to accurately track catheter tips and balloon markers in X-ray interventional images.
A Patient-Specific Framework for Autonomous Spinal Fixation via a Steerable Drilling Robot
Sharma, Susheela (University of Texas at Austin), Alambeigi, Farshid (Technical University of Munich)
Robotic IntelligencePoint CloudBiomedical DataComputed Tomography
🎯 What it does: A patient-specific spinal fixation frame based on CT-SDR (coaxial tube steerable drilling robot) was developed, achieving a J-shaped drilling trajectory for the first time.
A Refer-and-Ground Multimodal Large Language Model for Biomedicine
Huang, Xiaoshuang (Baidu Inc), Liu, Jia (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
Object DetectionSegmentationTransformerLarge Language ModelSupervised Fine-TuningVision Language ModelImageTextMultimodalityBiomedical DataMagnetic Resonance ImagingComputed TomographyUltrasound
🎯 What it does: A medical imaging reference-localization instruction tuning dataset, Med-GRIT-270k, was constructed, and the BiRD (Biomedical Refer-and-Ground Multimodal Large Language Model) model was fine-tuned based on this dataset to address the lack of reference and localization capabilities in biomedical multimodal models.
A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization
Playout, Clément, Cheriet, Farida (Polytechnique Montreal)
ClassificationSegmentationTransformerImage
🎯 What it does: Perform SLIC superpixel segmentation on retinal images, extract feature vectors for each superpixel, and use them as tokens for DR grading in the Vision Transformer.
A Scanning Laser Ophthalmoscopy Image Database and Trustworthy Retinal Disease Detection Method
Hu, Yichen (Central South University), Liu, Qing (University of Oulu)
ClassificationRecognitionConvolutional Neural NetworkContrastive LearningImage
🎯 What it does: A large-scale SLO imaging database, Retina-SLO, has been constructed, and TrustDetector has been proposed for reliable detection of various retinal diseases.
A task-conditional mixture-of-experts model for missing modality segmentation
Novosad, Philip (Genentech Inc.), Krishnan, Anitha Priya (Genentech Inc.)
SegmentationKnowledge DistillationConvolutional Neural NetworkMixture of ExpertsMultimodalityBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper addresses the issue of decreased segmentation performance caused by missing modalities in multimodal MRI. It proposes a model based on task-conditioned Mixture of Experts (MoE) and online self-distillation to achieve high-quality segmentation of multiple sclerosis lesions in the absence of any modality.
A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness
Chen, Boqi (University of North Carolina at Chapel Hill), Niethammer, Marc (UCSD)
TransformerMultimodalityTabularBiomedical Data
🎯 What it does: A unified longitudinal multimodal multi-view prediction model is proposed, which can flexibly handle missing data and inputs at any time point.
A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning
El Amrani, Nafie (University of Bonn), Bernard, Florian (University of Bonn)
SegmentationGenerationBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A completely unsupervised deep learning framework is proposed for constructing statistical shape models (SSM) of medical anatomical structures, achieving high-quality shape matching and deformation through both correspondence and deformation methods.
A Wasserstein Recipe for Replicable Machine Learning on Functional Neuroimages
Ding, Jiaqi (University of North Carolina at Chapel Hill), Wu, Guorong (POSTECH)
RecognitionDomain AdaptationTransformerBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A dual-branch Transformer based on Wasserstein distance is proposed to address the reproducibility issue in task recognition in functional magnetic resonance imaging (fMRI).
A Weakly-supervised Multi-lesion Segmentation Framework Based on Target-level Incomplete Annotations
Ju, Jianguo (Northwest University), Guan, Ziyu (Northwest University)
SegmentationConvolutional Neural NetworkImageBiomedical DataComputed Tomography
🎯 What it does: A weakly supervised multi-lesion segmentation framework based on Target-level Incomplete Annotations (TIA) is proposed, which can segment Crohn's disease CT images with very few annotations.
ABP: Asymmetric Bilateral Prompting for Text-guided Medical Image Segmentation
Zeng, Xinyi (Sichuan University), Wang, Yan (East China Normal University)
SegmentationTransformerPrompt EngineeringImageBiomedical DataComputed Tomography
🎯 What it does: A text-prompted medical image segmentation method is proposed, utilizing asymmetric bidirectional prompts to achieve mutual optimization between vision and text.
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
Chen, Qi, Xiong, Zhiwei (University of Science and Technology of China)
RestorationOptimizationConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A frequency-domain and spatial-domain mutual learning network, FSMNet, is proposed to accelerate the reconstruction of multi-contrast MRI.
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
Kong, Qingpeng (University of Notre Dame), Shi, Yiyu (University of Notre Dame)
ClassificationConvolutional Neural NetworkSupervised Fine-TuningImage
🎯 What it does: A channel pruning framework based on Soft Nearest Neighbor Loss (SNNL) is proposed to enhance the fairness of skin disease diagnosis models by identifying and removing channels that significantly affect sensitive attributes.
ACLNet: A Deep Learning Model for ACL Rupture Classification Combined with Bone Morphology
Liu, Chao (Peking University), Jiang, Tingting (Peking University)
ClassificationConvolutional Neural NetworkTransformerImagePoint CloudBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper proposes the ACLNet model, which combines MRI image sequences with knee joint bone morphology point clouds for the classification of anterior cruciate ligament (ACL) injuries.
AcneAI: A new acne severity assessment method using digital images and deep learning
Gazeau, Léa (Torus AI), Wolfe, Jonathan (BelleTorus Corporation)
ClassificationObject DetectionSegmentationConvolutional Neural NetworkImage
🎯 What it does: The AcneAI system has been developed to achieve full-process automatic processing of facial acne images: first segmenting all acne and acne-like lesions, then scoring each lesion, and finally synthesizing an overall severity score from 0 to 100.
Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding
Aggarwal, Himanshu (Inria), Thirion, Bertrand (Université Paris-Saclay)
ClassificationRecognitionBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper improves the accuracy of fMRI brain state decoding through stacked ensemble learning, utilizing classifiers pre-trained on other subjects, and compares it with traditional single-subject training methods.
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Khanal, Bidur (Rochester Institute of Technology), Linte, Cristian (Rochester Institute of Technology)
ClassificationConvolutional Neural NetworkImageBiomedical Data
🎯 What it does: A two-stage process is proposed: first, clean samples are identified using Co-Teaching and Gradient Variance (VOG), and then residual noisy labels are gradually cleaned through expert polling-based active learning to enhance the robustness of imbalanced medical image classification.
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
Chowdhury, Townim F. (University of Adelaide), Liao, Zhibin (University of Adelaide)
Domain AdaptationExplainability and InterpretabilityLarge Language ModelPrompt EngineeringBiomedical Data
🎯 What it does: This paper proposes AdaCBM, an interpretable diagnostic model that combines CLIP with the Concept Bottleneck Model (CBM) and inserts learnable adapters between the two, utilizing GPT-4 for prompt-based concept generation and filtering.
Adapting Pre-trained Generative Model to Medical Image for Data Augmentation
Yuan, Zhouhang (Zhejiang University), Li, Yingming (Zhejiang University)
GenerationData SynthesisTransformerGenerative Adversarial NetworkImageBiomedical Data
🎯 What it does: In a limited number of medical imaging scenarios, a pre-trained MAGE generative model is fine-tuned using adapters and vector quantization loss to generate high-quality medical images for data augmentation.
Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification
Ma, Siteng (University College Dublin), Dong, Ruihai (University College Dublin)
ClassificationConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: An adaptive query strategy based on curriculum learning, ACAL, is proposed for active learning in medical image classification.
Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis
Biswas, Koushik (Northwestern University), Bagci, Ulas (Northwestern University)
ClassificationSegmentationConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance ImagingComputed TomographyBenchmark
🎯 What it does: The Adaptive Smooth Activation Unit (ASAU) is proposed as a new smooth activation function, applied to multi-class disease classification and liver segmentation tasks in medical imaging.
Adaptive Subtype and Stage Inference for Alzheimer’s Disease
Wang, Xinkai (University of Southern California), Shi, Yonggang (University of Southern California)
Biomedical DataPositron Emission TomographyAlzheimer's Disease
🎯 What it does: The SSED algorithm is proposed, improving SuStaIn to achieve subtype-specific event learning, thereby better modeling the progression of Alzheimer's disease.
Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning
Yang, Yanwu (University of Tübingen), Ma, Ting (Harbin Institute of Technology at Shenzhen)
ClassificationKnowledge DistillationConvolutional Neural NetworkBiomedical DataMagnetic Resonance ImagingAlzheimer's Disease
🎯 What it does: An advanced self-adaptive self-supervised knowledge distillation framework (PSPD) is proposed for brain imaging classification tasks.
Advancing H&E-to-IHC Virtual Staining with Task-Specific Domain Knowledge for HER2 Scoring
Peng, Qiong (Xiamen University), Wang, Liansheng (Shanghai Changhai Hospital)
Image TranslationGenerationGenerative Adversarial NetworkImage
🎯 What it does: This paper proposes a GAN-based virtual staining method that converts H&E stained images into HER2 IHC stained images, achieving precise retention of nuclear distribution and membrane staining intensity through a kernel density estimator and a membrane staining enhancement branch.
Advancing Sensorless Freehand 3D Ultrasound Reconstruction with a Novel Coupling Pad
Dai, Ling (Xi'an Jiaotong University), Liang, Libin (Xi'an Jiaotong University)
Pose EstimationOptimizationBiomedical DataUltrasound
🎯 What it does: This paper develops a three N-shaped line coupling pad that can be directly used for traditional two-dimensional ultrasound scanning and proposes a coarse-fine optimization method based on distance-topological difference reduction, achieving sensor-free freehand 3D ultrasound reconstruction.
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning
Han, Woojung (Yonsei University), Hwang, Seong Jae (Mediwhale)
GenerationOptimizationConvolutional Neural NetworkReinforcement LearningDiffusion modelImageTextMultimodalityBiomedical Data
🎯 What it does: This paper proposes the CXRL framework, which refines text-driven chest X-ray image generation (report-to-CXR) through reinforcement learning, achieving high-fidelity alignment between generated images and diagnostic reports.
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Wang, Hongqiu (Hong Kong University of Science and Technology), Zhu, Lei (Hong Kong University of Science and Technology)
SegmentationDomain AdaptationConvolutional Neural NetworkImage
🎯 What it does: To address the problem of vessel segmentation in ultra-wide field scanning laser ophthalmoscope (UWF-SLO) images, the authors propose a patch-based source-free active domain adaptation (SFADA) framework and design a cascading uncertainty-dominant (CUP) selection strategy, which can significantly enhance cross-center segmentation performance with a minimal amount of annotated patches.
Adversarial Diffusion Model for Domain-Adaptive Depth Estimation in Bronchoscopic Navigation
Yang, Yiguang, Liao, Hongen (Tsinghua University)
Depth EstimationDomain AdaptationConvolutional Neural NetworkDiffusion modelGenerative Adversarial NetworkImage
🎯 What it does: A two-stage adversarial diffusion model is proposed, which achieves domain adaptation for depth estimation of bronchoscopy images through feature-level adversarial learning after training in a virtual domain.
Affinity Learning Based Brain Function Representation for Disease Diagnosis
Liu, Mengjun (Shanghai Jiao Tong University), Wang, Qian (United Imaging Intelligence)
ClassificationRepresentation LearningGraph Neural NetworkLarge Language ModelSupervised Fine-TuningBiomedical DataMagnetic Resonance ImagingAlzheimer's Disease
🎯 What it does: A brain function representation framework based on affinity learning is proposed, using randomly sampled 3D patches to obtain an information-rich functional connectivity matrix through self-supervised learning, which is then used for disease diagnosis.
Airway segmentation based on topological structure enhancement using multi-task learning
Yang, Xuan (Shenzhen University), Liao, Hongen (Tsinghua University)
SegmentationConvolutional Neural NetworkImageBiomedical DataComputed Tomography
🎯 What it does: This paper proposes a 3D U-Net framework based on multi-task learning, using centerline detection as an auxiliary task to enhance the topological integrity of airway segmentation. It further improves branch connectivity and the identification of small bronchi through a newly established Topological Embedding Interaction Module (TEIM) and a Topological Enhanced Attention Module (TEAM).
Algebraic Sphere Surface Fitting for Accurate and Efficient Mesh Reconstruction from Cine CMR Images
He, Jin (Beijing Normal University), Wang, Shuo (Fudan University)
SegmentationGenerationComputational EfficiencyImagePoint CloudMeshMagnetic Resonance Imaging
🎯 What it does: A framework for reconstructing the ventricular surface based on algebraic spherical fitting is proposed, which can directly generate a complete three-dimensional mesh from a sparse layered wireframe point cloud of a single frame cine CMR image.
Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias
Ansari, Faizanuddin (Indian Statistical Institute), Das, Swagatam (Indian Statistical Institute)
ClassificationConvolutional Neural NetworkSupervised Fine-TuningImage
🎯 What it does: A data augmentation framework that combines adaptive sampling and Mixup is proposed to simultaneously alleviate skin tone bias and class imbalance, improving the accuracy and fairness of skin lesion classification.
Aligning and Restoring Imperfect ssEM images for Continuity Reconstruction
Lv, Yanan (Chinese Academy of Sciences), Han, Hua (Chinese Academy of Sciences)
RestorationSegmentationOptical FlowImageBiomedical Data
🎯 What it does: This study investigates an automatic alignment and repair method for slices with cracks or folding defects in serial slice electron microscopy (ssEM), aiming to enhance the continuity and accuracy of large-volume neural tissue reconstruction.
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
Gao, Yunhe (Rutgers University), Metaxas, Dimitris (Rutgers University)
ClassificationExplainability and InterpretabilityTransformerLarge Language ModelVision Language ModelContrastive LearningImageBiomedical Data
🎯 What it does: Proposes the Explicd framework, which transforms LLM or expert knowledge into diagnostic criteria and learns visual concepts in visual language models to achieve interpretable medical image classification.
Aligning Medical Images with General Knowledge from Large Language Models
Fang, Xiao (HKUST), Chen, Hao (Harvard Medical School)
ClassificationExplainability and InterpretabilityTransformerLarge Language ModelPrompt EngineeringContrastive LearningImageBiomedical DataComputed Tomography
🎯 What it does: This paper proposes a visual symptom-based prompt learning framework called ViP, which utilizes large language models to generate interpretable visual symptoms for medical images and achieves transfer learning of CLIP in medical image classification tasks through a dual prompt network (context prompt CoP and aggregation prompt MeP).
All-In-One Medical Image Restoration via Task-Adaptive Routing
Yang, Zhiwen (Beihang University), Xu, Yan (Beihang University)
RestorationSuper ResolutionConvolutional Neural NetworkMixture of ExpertsImageBiomedical DataMagnetic Resonance ImagingComputed TomographyPositron Emission Tomography
🎯 What it does: A full-task medical image restoration network called AMIR is proposed, which can simultaneously accomplish MRI super-resolution, CT denoising, and PET synthesis within a single model.
AMONuSeg: A Histological Dataset for African Multi-Organ Nuclei Semantic Segmentation
Zerouaoui, Hasnae (Mohammed VI Polytechnic University), Navab, Nassir (Technische Universität München)
SegmentationTransformerContrastive LearningImageBiomedical Data
🎯 What it does: The first publicly available African multi-organ H&E stained tissue nucleus segmentation dataset, AMONuSeg, collected using low-resource microscopy, is proposed and evaluated with various existing segmentation models and stain normalization methods.
An approach to building foundation models for brain image analysis
Karimi, Davood (Boston Children's Hospital)
RestorationSegmentationGenerationTransformerImageBiomedical DataMagnetic Resonance ImagingDiffusion Tensor Imaging
🎯 What it does: A self-supervised foundational model based on Transformer has been constructed for brain image generation and structural learning, avoiding reliance on a large amount of labeled data.
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
Li, Qing (Fudan University), Wang, Chengyan (Fudan University)
SegmentationImageBiomedical DataMagnetic Resonance ImagingComputed TomographyBenchmark
🎯 What it does: A systematic evaluation of the fairness of foundational models (SAM, Medical SAM, SAT-Nano) in multi-organ medical image segmentation.
An Evaluation of State-of-the-Art Projectors in the Presence of Noise and Nonlinearity in the Beer-Lambert Law
Xie, Shiyu (University of Florida), Entezari, Alireza (University of Florida)
ImageComputed Tomography
🎯 What it does: In the context of low-dose/limited-angle CT reconstruction considering the Beer-Lambert nonlinearity and Poisson noise, the reconstruction errors of three mainstream fast projectors (SF, LTRI, CNSF) are evaluated against an accurate projector.
An MR-Compatible Virtual Reality System for Assessing Neuronal Plasticity of Sensorimotor Neurons and Mirror Neurons
Wang, Xiaocheng (Zhejiang University), Xu, Dongrong (Columbia University)
Biomedical DataMagnetic Resonance Imaging
🎯 What it does: A MR-compatible VR system (MR.VRA) has been developed to perform real-time virtual reality (VR) mirror therapy (MT) within an MRI scanner, aimed at assessing upper limb motor function and activating related motor and mirror neurons.
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis
Elbatel, Marawan, Li, Xiaomeng (Southern Medical University)
ClassificationGenerationData SynthesisDiffusion modelImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A generative model based on cellular automata, GeCA, is introduced for high-resolution image synthesis and enhanced classification of retinal diseases.
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
Luo, Zihao (University of Electronic Science and Technology of China), Wang, Guotai (University Of Electronic Science And Technology Of China)
SegmentationDomain AdaptationConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: An Active Source Free Domain Adaptation (ASFDA) framework UGTST is proposed for prostate MRI segmentation.
Analyzing Adjacent B-Scans to Localize Sickle Cell Retinopathy In OCTs
Bhattarai, Ashuta (University of Delaware), Kambhamettu, Chandra (University of Delaware)
Object DetectionTransformerContrastive LearningImageBiomedical DataComputed Tomography
🎯 What it does: A two-stage network CSAT based on transformers is proposed for detecting sickle cell retinopathy (SCR) and macular location on OCT B-scans.
Analyzing Cross-Population Domain Shift in Chest X-Ray Image Classification and Mitigating the Gap with Deep Supervised Domain Adaptation
Musa, Aminu (Federal University Dutse), Lawal, Yusuf (Aragon Institute on Engineering Research)
ClassificationDomain AdaptationConvolutional Neural NetworkSupervised Fine-TuningImage
🎯 What it does: Analyze the domain shift problem in cross-population chest X-ray image classification and improve classification performance on Nigerian data through supervised adversarial domain adaptation.
Anatomic-constrained Medical Image Synthesis via Physiological Density Sampling
Chu, Yuetan, Gao, Xin (Chinese Academy of Sciences)
SegmentationGenerationData SynthesisReinforcement LearningGenerative Adversarial NetworkImageBiomedical DataComputed Tomography
🎯 What it does: This paper proposes a medical image synthesis framework based on anatomical constraints, ACIS, which uses pseudo-masks as anatomical priors to generate diverse and realistic CT images along with corresponding annotations. It significantly enhances segmentation and reconstruction performance in few-shot learning through post-processing refinement using registration uncertainty.
Anatomical Positional Embeddings
Goncharov, Mikhail (Skolkovo Institute of Science and Technology), Oseledets, Ivan (Skolkovo Institute of Science and Technology)
SegmentationRetrievalConvolutional Neural NetworkContrastive LearningImageBiomedical DataComputed Tomography
🎯 What it does: A self-supervised 3D Anatomical Position Embedding (APE) model is proposed, capable of generating three-dimensional position embeddings for each voxel in medical CT images at once, and applying it to anatomical label retrieval and few-shot organ localization.
Anatomical Structure-Guided Medical Vision-Language Pre-training
Li, Qingqiu (Fudan University), Wang, Shujun (Hong Kong Polytechnic University)
ClassificationObject DetectionSegmentationConvolutional Neural NetworkTransformerContrastive LearningImageTextBiomedical Data
🎯 What it does: This paper proposes an anatomy structure-guided medical vision-language pre-training framework, which first parses reports into <anatomical region, findings, presence> triples, aligns the anatomical regions with sentences, and enhances internal and external representations using an image-label recognition decoder and soft label contrastive learning.
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Konz, Nicholas (Duke University), Mazurowski, Maciej A. (Duke University)
SegmentationGenerationData SynthesisDiffusion modelImageBiomedical DataMagnetic Resonance ImagingComputed Tomography
🎯 What it does: A medical image generation method based on diffusion models is proposed, which can strictly follow the given multi-class anatomical segmentation masks at each sampling step, supporting partial mask conditions and adjusting the similarity between generated images and real images through latent space interpolation.
Anatomically-Guided Segmentation of Cerebral Microbleeds in T1-weighted and T2*-weighted MRI
Kwon, Junmo (Sungkyunkwan University), Park, Hyunjin (VUNO Inc.)
Object DetectionSegmentationConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper achieves automatic segmentation and detection of cerebral microbleeds by incorporating an anatomical proxy task (segmenting the brainstem, deep structures, and the lower ventricular regions) into the CMB segmentation network.
Anatomy-Aware Gating Network for Explainable Alzheimer’s Disease Diagnosis
Jiang, Hongchao (Nanyang Technological University), Miao, Chunyan (Nanyang Technological University)
ClassificationExplainability and InterpretabilityConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance ImagingAlzheimer's Disease
🎯 What it does: A gated network based on anatomical information (AAGN) has been designed and implemented, achieving interpretable Alzheimer's disease diagnosis by dynamically selecting features from different brain regions.
Anatomy-guided Pathology Segmentation
Jaus, Alexander (Karlsruhe Institute of Technology), Kleesiek, Jens (Institute for AI in Medicine, University Hospital Essen)
SegmentationTransformerImageBiomedical DataComputed TomographyPositron Emission Tomography
🎯 What it does: A general model APEx is proposed to enhance pathological segmentation using anatomical knowledge, combining anatomical and pathological query interactions.
APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy
Sheng, Yi (George Mason University), Yang, Lei (University of North Carolina at Chapel Hill)
RestorationSuper ResolutionComputational EfficiencyConvolutional Neural NetworkAuto EncoderImageBiomedical DataComputed TomographyUltrasound
🎯 What it does: This paper proposes the APS-USCT framework, which first uses the AI model APS-wave to upsample sparse ultrasound waveforms to dense waveforms, and then directly reconstructs the sound speed (SOS) image using APS-FWI (based on InversionNet with source encoding and optional SE-Block).
Are We Ready for Out-of-Distribution Detection in Digital Pathology?
Oh, Ji-Hun (University of Illinois Urbana Champaign), Bhargava, Rohit (University of Illinois Urbana Champaign)
Anomaly DetectionConvolutional Neural NetworkTransformerSupervised Fine-TuningImageBiomedical DataBenchmark
🎯 What it does: Conducted benchmark experiments on out-of-distribution detection in digital pathology (S-OODD and MC-OODD), systematically evaluating various detectors, transfer learning strategies, and model architectures.
ASA: Learning Anatomical Consistency, Sub-volume Spatial Relationships and Fine-grained Appearance for CT Images
Pang, Jiaxuan (Arizona State University), Liang, Jianming (Arizona State University)
SegmentationTransformerContrastive LearningImageBiomedical DataComputed Tomography
🎯 What it does: A self-supervised learning framework for CT images, called ASA, is proposed to learn anatomical consistency, sub-volume spatial relationships, and fine-grained appearance features, ultimately enhancing the performance of multi-organ and single-organ segmentation.
ASPS: Augmented Segment Anything Model for Polyp Segmentation
Li, Huiqian (University of Science and Technology of China), Han, Junwei (Xi'an Jiaotong University)
SegmentationDomain AdaptationConvolutional Neural NetworkTransformerImageComputed Tomography
🎯 What it does: This paper proposes the ASPS (Augmented Segment Anything Model for Polyp Segmentation) model, which enhances multi-scale feature extraction and domain adaptation capabilities for colonoscopic images by adding a trainable CNN encoder branch and uncertainty-guided prediction regularization based on SAM.
Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks
Raj, Ankita, Arora, Chetan (PGIMER Chandigarh)
ClassificationSegmentationKnowledge DistillationConvolutional Neural NetworkContrastive LearningImageBiomedical DataComputed Tomography
🎯 What it does: This paper proposes a multi-task deep learning framework for joint segmentation, classification, and alignment, achieving efficient learning for the detection of malignant and benign lung nodules through a teacher-student strategy and mixed loss.
Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment
Fang, Yuqi (University of North Carolina at Chapel Hill), Liu, Mingxia (University of North Carolina at Chapel Hill)
ClassificationConvolutional Neural NetworkGraph Neural NetworkTransformerImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper proposes an attention-enhanced framework for the fusion of structural and functional MRI for the early diagnosis of HIV-related asymptomatic cognitive impairment.
Automated Robust Muscle Segmentation in Multi-level Contexts using a Probabilistic Inference Framework
Wang, Jinge (Peking University), Zhang, Terry Jianguo (Peking Union Medical College Hospital)
SegmentationPose EstimationImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: Proposes an automated 3D paravertebral muscle segmentation and reconstruction method based on a probabilistic inference framework.
Automated Spinal MRI Labelling from Reports Using a Large Language Model
Park, Robin Y. (University of Oxford), Zisserman, Andrew (University of Oxford)
ClassificationConvolutional Neural NetworkLarge Language ModelPrompt EngineeringTextBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper proposes a general pipeline based on large language models that can automatically extract structured labels from radiology reports.
Automatic Mandibular Semantic Segmentation of Teeth Pulp Cavity and Root Canals, and Inferior Alveolar Nerve on Pulpy3D Dataset
Gamal, Mahmoud (Alexandria University), Torki, Marwan (Alexandria University)
SegmentationConvolutional Neural NetworkImageBiomedical DataComputed Tomography
🎯 What it does: A complete 3D CBCT semantic segmentation system for the mandibular pulp chamber, root canal, and inferior alveolar nerve (IAN) has been developed, and the Pulpy3D dataset has been proposed.
AutoSkull: Learning-based Skull Estimation for Automated Pipelines
Milojevic, Aleksandar (ETH Zurich), Gözcü, Baran
Point CloudComputed Tomography
🎯 What it does: Predicting individualized cranial shapes from 3D facial scans to achieve radiation-free skeletal modeling.
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping
Liu, Yikang (United Imaging Intelligence), Sun, Shanhui (United Imaging Intelligence)
Object TrackingSegmentationConvolutional Neural NetworkTransformerVideoBiomedical DataElectrocardiogram
🎯 What it does: The study proposes an Auxiliary Input Training (AIT) method, using catheter masks as auxiliary inputs during training to help deep learning models better perform heart rate phase matching and catheter tip tracking, thereby improving the real-time navigation accuracy of dynamic coronary road mapping technology.
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Barfoot, Theodore (King's College London), Vercauteren, Tom (King's College London)
SegmentationConvolutional Neural NetworkBiomedical DataMagnetic Resonance Imaging
🎯 What it does: A differentiable auxiliary loss function mL1-ACE is proposed to enhance the confidence calibration of medical image segmentation models, and it is jointly trained with traditional losses (cross-entropy, Dice) on the BraTS 2021 dataset.
BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision
Bransby, Kit M. (Ultromics Ltd), Gomez, Alberto (Ultromics Ltd)
ClassificationSegmentationConvolutional Neural NetworkSupervised Fine-TuningContrastive LearningImageBiomedical DataUltrasound
🎯 What it does: The study proposes two random background mixing enhancement methods, BackMix and wBackMix, to address the shortcut learning problem caused by background information in echocardiogram view classification, achieving performance improvement under semi-supervised conditions with only 5% segmentation annotations.
Baikal: Unpaired Denoising of Fluorescence Microscopy Images using Diffusion Models
Chaudhary, Shivesh (Calico Life Sciences LLC), Xu, Jun (Heilongjiang University)
RestorationDiffusion modelImage
🎯 What it does: A denoising framework for fluorescence microscopy images called Baikal is constructed based on unaligned data, utilizing a Denoising Diffusion Probabilistic Model (DDPM) to learn the generative prior of clean images, and then recovering clean images from noisy images during inference through various conditional sampling strategies (Forward-Backward, Mixing, Repaint).
BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning
Hanif, Asif (Mohamed Bin Zayed University of Artificial Intelligence), Anwer, Rao Muhammad (Mohamed Bin Zayed University of Artificial Intelligence)
Adversarial AttackData-Centric LearningTransformerPrompt EngineeringContrastive LearningBiomedical DataMagnetic Resonance Imaging
🎯 What it does: This paper proposes a backdoor attack method called BAPLe that utilizes prompt learning to embed backdoors in medical foundation models (Med-FM), achieving efficient backdoor injection in data-scarce scenarios while keeping the model frozen.
Best of Both Modalities: Fusing CBCT and Intraoral Scan Data into a Single Tooth Image
Kim, SaeHyun (Korea University), Baek, Seung Jun (Korea University Anam Hospital)
Point CloudMeshComputed Tomography
🎯 What it does: Integrating CBCT and IOS dental images to generate a single complete dental mesh.
Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
Lin, Xian (Huazhong University of Science and Technology), Yan, Zengqiang (Huazhong University of Science and Technology)
SegmentationConvolutional Neural NetworkTransformerPrompt EngineeringImageBiomedical DataUltrasound
🎯 What it does: This paper presents SAMUS and its end-to-end automatic segmentation version AutoSAMUS, which improves the performance of SAM on ultrasound images by using parallel CNN branches, position/feature adapters, and cross-branch attention to supplement local information, achieving fully automatic segmentation through an automatic prompt generator.
BGDiffSeg: a Fast Diffusion Model for Skin Lesion Segmentation via Boundary Enhancement and Global Recognition Guidance
Guo, Yilin (Sun Yat-sen University), Cai, Qingling (Sun Yat-sen University)
SegmentationDiffusion modelGenerative Adversarial NetworkImageBiomedical Data
🎯 What it does: This paper proposes a fast skin lesion segmentation framework called BGDiffSeg based on diffusion models, aiming to achieve more accurate and clearer lesion boundary segmentation under limited computational resources and inference time.
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
Kang, Ming (Monash University), Phan, Raphaël C.-W. (Monash University)
Object DetectionConvolutional Neural NetworkImageBiomedical DataMagnetic Resonance Imaging
🎯 What it does: Proposed and implemented BGF-YOLO, an improved YOLOv8 network for brain tumor MRI detection.
BiasPruner: Debiased Continual Learning for Medical Image Classification
Bayasi, Nourhan (University of British Columbia), Garbi, Rafeef
ClassificationConvolutional Neural NetworkImageBiomedical Data
🎯 What it does: A continuous learning framework named BiasPruner is proposed, which consciously forgets bias-related network units to achieve fairness and performance improvement in medical image classification tasks.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
Chen, Yinda (University of Science and Technology of China), Xiong, Zhiwei (University of Science and Technology of China)
RetrievalTransformerLarge Language ModelContrastive LearningImageTextMultimodalityComputed Tomography
🎯 What it does: The first publicly available dataset of paired 3D CT images and radiology reports (BIMCV-R) has been constructed, and a multimodal retrieval framework MedFinder based on a dual-stream network and a large language model (BiomedCLIP) has been proposed.