Recent papers about medical images published on CVPR. [Github]

🌟🌟🌟To complement or correct it (highlight, oral, and so on), please contact me at 1729766533 [at] qq [dot] com or send a pull request.

Last updated: 2025/06/20

• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis. [Paper][Code]
• Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images. [Paper][Code]
• ZoomLDM: Latent Diffusion Model for multi-scale image generation. [Paper][Code]

• nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark. [Paper][Code]
• Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline. [Paper][Code]
• Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation. [Paper][Code]
• DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation. [Paper][Code]
• LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging. [Paper][Code]
• EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation. [Paper][Code]
• nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark. [Paper][Code]
• Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline. [Paper][Code]
• Advancing Generalizable Tumor Segmentation with Anomaly.Aware Open-Vocabulary Attention Maps and Frozen FoundationDiffusion Models. [Paper][Code]
• Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation. [Paper][Code]
• Boost the Inference with Co-training: A Depth-guided Mutual Learning Framework for Semi-supervised Medical Polyp Segmentation (RD-Net). [Paper][Code]
• Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation. [Paper][Code]

• Multi-modal Vision Pre-training for Medical Image Analysis. [Paper][Code]
• CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning [Paper][Code]
• EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance [Paper][Code]

• VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge. [Paper][Code]
• BIOMEDICA: An Open Biomedical Image-Caption Archive with Vision-Language Models derived from Scientific Literature. [Paper][Project]
• BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models. [Paper][Code]
• MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output. [Paper][Code]
• Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis. [Paper][Code]
• Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering. [Paper][Code]
• Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation. [Paper][Code]
• FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification. [Paper][Code]
• MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Disrete Visual Representations. [Paper][Code]

• Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance LearningComputational Pathology. [Paper][Code]
• FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification. [Paper][Code][推送]
• Distilled Prompt Learning for Incomplete Multimodal Survival Prediction. [Paper][Code]
• Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning. [Paper][Code]
• SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding. [Paper][Code]
• 2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification. [Paper][Code]
• CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology. [Paper][Code]
• MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images. [Paper][Code]
• HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. [Paper][Code]
• M3amba: Memory Mamba is All You Need for Whole Slide Image Classification. [Paper][Code]
• Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging. [Paper][Code]
• BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology. [Paper][Code]
• Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation. [Paper][Code]
• TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model. [Paper][Code]
• Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information. [Paper][Code]
• Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder. [Paper][Code]
• MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification. [Paper][Code]
• Learning Heterogeneous Tissues with Mixture of Experts for Gigapixel Whole Slide Images. [Paper][Code]
• Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning. [Paper][Code]
• WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression. [Paper][Code]

• Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression.
• Towards All-in-One Medical Image Re-Identification. [Paper][Code]
• OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection. [Paper][Code]
• MultiMorph: On-demand Atlas Construction. [Paper][Code]

• QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction. [Paper][Code][Project]
• Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI. [Paper][Code]
• Structure-Aware Sparse-View X-ray 3D Reconstruction.[Paper][Code]
• Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI. [Paper][Code]

• Learning Large-Factor EM Image Super-Resolution with Generative Priors. [Paper][Code][Video]
• CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data. [Paper][Code]

• Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration. [Paper]
• [Oral & Best Paper Candidate!!!] Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration. [Paper][Code]

• PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation. [Paper]
• Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation. [Paper]
• Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation. [Paper][Code]
• One-Prompt to Segment All Medical Images. [Paper][Code]
• Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention. [Paper][Code][Project]
• Diversified and Personalized Multi-rater Medical Image Segmentation. [Paper][Code]
• MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling. [Paper][Code]
• Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation. [Paper][Code]
• Cross-dimension Affinity Distillation for 3D EM Neuron Segmentation. [Paper][Code]
• ToNNO: Tomographic Reconstruction of a Neural Network’s Output for Weakly Supervised Segmentation of 3D Medical Images.[Paper][Code]
• Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation. [Paper][Code]
• Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge. [Paper][Code]
• Tyche: Stochastic in Context Learning for Universal Medical Image Segmentation. [Paper][Code]
• Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation. [Paper][Code]
• S2VNet: Universal Multi-Class Medical Image Segmentation via Clustering-based Slice-to-Volume Propagation. [Paper][Code]
• EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.[Paper][Code]
• Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation.[Paper][Code]
• ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting. [Paper][Code]
• [Oral!!!] Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration. [Paper][Code]
• PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise Hardness. [Paper][Code][Video]

• Learned representation-guided diffusion models for large-image generation. [Paper][Code]
• MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant. [Paper]
• Towards Generalizable Tumor Synthesis. [Paper][Code]
• Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images. [Paper][Code]

• Systematic comparison of semi-supervised and self-supervised learning for medical image classification. [Paper][Code]
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images. [Paper][Code]

• Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts. [Paper]

• VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis. [Paper][Code]
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning. [Paper]
• [Highlight!] Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning. [Paper][Code]
• Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models. [Paper][Code]
• Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding. [Paper][Code]
• Low-Rank Knowledge Decomposition for Medical Foundation Models. [Paper][Code]

• PairAug: What Can Augmented Image-Text Pairs Do for Radiology? [Paper][Code]
• Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Matching Framework. [Paper][Code]
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images. [Paper][Code]
• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM. [Paper][Code]
• CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification. [Paper][Code]
• FairCLIP: Harnessing Fairness in Vision-Language Learning [Paper][Code][推送]

• Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction. [Paper]
• Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology. [Paper][Code]
• PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation. [Paper]
• ChAda-ViT: Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images. [Paper][Code]
• SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology. [Paper][Code]
• Transcriptomics-guided Slide Representation Learning in Computational Pathology [Paper][Code]

• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling. [Paper]
• FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders. [Paper][Code]

• Some CVPR 2025 papers sourced from https://github.com/cerishleon/cvpr25_medical_paper