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: 2024/05/17

• 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]
• 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!!!] 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]

• Learned representation-guided diffusion models for large-image generation. [Paper]
• 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]

• 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]

• Low-Rank Knowledge Decomposition for Medical Foundation Models. [Paper][Code]

• 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]

• 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]