轉(zhuǎn)自：第一作者EDesk

2 0 24 

● CVPR (CCF-A).

● 來源：https://github.com/MedAIerHHL/CVPR-MIA （持續(xù)更新，已授權(quán)）

CVPR-MIA

Image Reconstruction (圖像重建) 

• QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction.

• 中文：QN-Mixer：用于稀疏視圖CT重建的擬牛頓MLP-Mixer模型
• Paper: https://arxiv.org/abs/2402.17951v1
• Project: https://towzeur.github.io/QN-Mixer/

• Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI.

• 中文：單棧MRI的全卷積切片到體積重建
• Paper: https://arxiv.org/abs/2312.03102
• Code: http://github.com/seannz/svr

• Structure-Aware Sparse-View X-ray 3D Reconstruction.

• 中文：結(jié)構(gòu)感知稀疏視圖 X 射線 3D 重建
• Paper: https://arxiv.org/abs/2311.10959
• Code: https://github.com/caiyuanhao1998/SAX-NeRF

• Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI.

• 中文：通過子采樣分解的漸進(jìn)分治以加速MRI
• Paper: https://arxiv.org/abs/2403.10064
• Code: https://github.com/ChongWang1024/PDAC

 

Image Resolution (圖像超分) 

• Learning Large-Factor EM Image Super-Resolution with Generative Priors

• 中文：使用生成先驗學(xué)習(xí)大因子電磁圖像超分辨率
• Paper: https://openaccess.thecvf.com/content/CVPR2024/papers/Shou_Learning_Large-Factor_EM_Image_Super-Resolution_with_Generative_Priors_CVPR_2024_paper.pdf
• Code: https://github.com/jtshou/GPEMSR
• Video: https://youtu.be/LNSLQM5-YcM

• CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

• 中文：CycleINR：任意尺度醫(yī)學(xué)數(shù)據(jù)體素超分辨率的循環(huán)隱式神經(jīng)表示    
• Paper: https://arxiv.org/abs/2404.04878v1   

  
  

Image Registration (圖像配準(zhǔn))    

• Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration 

• 中文：適用于可變形多模態(tài)醫(yī)學(xué)圖像配準(zhǔn)的模態(tài)無關(guān)結(jié)構(gòu)圖像表示學(xué)習(xí)

• Paper: https://arxiv.org/abs/2402.18933

• [Oral & Best Paper Candidate!!!] Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

• 中文：基于相關(guān)性的粗到細(xì)MLP用于可變形醫(yī)學(xué)圖像配準(zhǔn)
• Paper: https://arxiv.org/abs/2406.00123
• Code: https://github.com/jungeun122333/UVI-Net

 

Image Segmentation (圖像分割)

• PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

• 中文：PrPSeg：全景腎病病理分割的通用命題學(xué)習(xí)
• Paper: https://arxiv.org/abs/2402.19286

• Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

• 中文：通過模型自我消歧學(xué)習(xí)的多功能醫(yī)學(xué)圖像分割，來自多源數(shù)據(jù)集
• Paper: https://arxiv.org/abs/2311.10696

• Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation

• 中文：每個測試圖像應(yīng)得到特定提示：2D醫(yī)學(xué)圖像分割的持續(xù)測試時適應(yīng)
• Paper: https://arxiv.org/abs/2311.18363
• Code: https://github.com/Chen-Ziyang/VPTTA

• One-Prompt to Segment All Medical Images

• 中文：一提示分割所有醫(yī)學(xué)圖像
• Paper: https://arxiv.org/abs/2305.10300
• Code: https://github.com/WuJunde/PromptUNet/tree/main

• Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention

• 中文：基于多尺度注意力的多頻率模態(tài)無關(guān)醫(yī)學(xué)圖像分割
• Paper: https://arxiv.org/abs/2405.06284
• Code Project: https://skawngus1111.github.io/MADGNet_project/

• Diversified and Personalized Multi-rater Medical Image Segmentation

• 中文：多樣化和個性化的多評分員醫(yī)學(xué)圖像分割
• Paper: https://arxiv.org/pdf/2212.00601
• Code: https://github.com/ycwu1997/D-Persona

• MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

• 中文：基于3D遮罩自動編碼和偽標(biāo)簽的MAPSeg：異構(gòu)醫(yī)學(xué)圖像分割的統(tǒng)一無監(jiān)督域適應(yīng)
• Paper: https://arxiv.org/abs/2303.09373    

• Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation

• 中文：半監(jiān)督醫(yī)學(xué)圖像分割的自適應(yīng)雙向位移
• Paper: https://arxiv.org/abs/2405.00378
• Code: https://github.com/chy-upc/ABD

• Cross-dimension Affinity Distillation for 3D EM Neuron Segmentation

• 中文：3D EM神經(jīng)元分割的跨維度親和力蒸餾
• Paper: https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Cross-Dimension_Affinity_Distillation_for_3D_EM_Neuron_Segmentation_CVPR_2024_paper.pdf
• Code: https://github.com/liuxy1103/CAD

• ToNNO: Tomographic Reconstruction of a Neural Network’s Output for Weakly Supervised Segmentation of 3D Medical Images.

• 中文：ToNNO：神經(jīng)網(wǎng)絡(luò)輸出的斷層重建用于弱監(jiān)督3D醫(yī)學(xué)圖像分割
• Paper: https://arxiv.org/abs/2405.06880

• Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

• 中文：通過模型自我消歧學(xué)習(xí)的多功能醫(yī)學(xué)圖像分割，來自多源數(shù)據(jù)集
• Paper: https://arxiv.org/abs/2311.10696

• Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge

• 中文：基于人類先驗知識的正畸治療高效實例分割框架
• Paper: https://arxiv.org/abs/2404.01013

• Tyche: Stochastic in Context Learning for Universal Medical Image Segmentation

• 中文：Tyche：上下文中的隨機學(xué)習(xí)用于通用醫(yī)學(xué)圖像分割
• Paper: https://arxiv.org/abs/2401.13650
• Code: https://github.com/mariannerakic/tyche/

• Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

• 中文：混合域半監(jiān)督醫(yī)學(xué)圖像分割中中間域的構(gòu)建與探索
• Paper: https://arxiv.org/abs/2404.08951
• Code: https://github.com/MQinghe/MiDSS

• S2VNet: Universal Multi-Class Medical Image Segmentation via Clustering-based Slice-to-Volume Propagation
• 中文：S2VNet：通過聚類基礎(chǔ)的切片到體積傳播實現(xiàn)通用多類別醫(yī)學(xué)圖像分割
• Paper: https://arxiv.org/abs/2403.16646
• Code: https://github.com/dyh127/S2VNet
• EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.

• 中文：EMCAD：醫(yī)學(xué)圖像分割的高效多尺度卷積注意力解碼
• Paper: https://arxiv.org/abs/2405.06880
• Code: https://github.com/SLDGroup/EMCAD

• Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation.

• 中文：像住院醫(yī)生一樣訓(xùn)練：情境先驗學(xué)習(xí)導(dǎo)向的通用醫(yī)學(xué)圖像分割    
• Paper: https://arxiv.org/abs/2306.02416
• Code: https://github.com/yhygao/universal-medical-image-segmentation

• ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

• Paper: https://arxiv.org/abs/2312.04964

• [Oral!!!] Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

• Paper: https://github.com/dengxl0520/MemSAM/blob/main/paper.pdf
• Code: https://github.com/dengxl0520/MemSAM/tree/main

• PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise Hardness

• Paper: https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_PH-Net_Semi-Supervised_Breast_Lesion_Segmentation_via_Patch-wise_Hardness_CVPR_2024_paper.pdf
• Code: https://github.com/jjjsyyy/PH-Net
• Video: https://cvpr.thecvf.com/virtual/2024/poster/30539

 

Image Generation (圖像生成)  

• Learned representation-guided diffusion models for large-image generation

• 中文：用于大型圖像生成的學(xué)習(xí)表示指導(dǎo)的擴散模型
• Paper: https://arxiv.org/abs/2312.07330

• MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant

• 中文：MedM2G：通過視覺不變的交叉引導(dǎo)擴散統(tǒng)一醫(yī)療多模式生成
• Paper: https://arxiv.org/html/2403.04290v1

• Towards Generalizable Tumor Synthesis

• 中文：邁向泛化腫瘤合成
• Paper: https://arxiv.org/abs/2402.19470v1
• Code: https://github.com/MrGiovanni/DiffTumor

• Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images

• 中文：無需任何中間幀的4D醫(yī)學(xué)圖像的數(shù)據(jù)高效無監(jiān)督插值
• Paper: https://arxiv.org/abs/2404.01464
• Code: https://github.com/jungeun122333/UVI-Net

 

Image Classification (圖像分類)  

• Systematic comparison of semi-supervised and self-supervised learning for medical image classification

• 中文：醫(yī)學(xué)圖像分類中半監(jiān)督和自監(jiān)督學(xué)習(xí)的系統(tǒng)比較
• Paper: https://arxiv.org/abs/2307.08919v2
• Code: https://github.com/tufts-ml/SSL-vs-SSL-benchmark

• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
• 中文：適應(yīng)視覺語言模型以在醫(yī)學(xué)圖像中實現(xiàn)泛化的異常檢測
• Paper: https://arxiv.org/abs/2403.12570
• Code: https://github.com/MediaBrain-SJTU/MVFA-AD  
• FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders

• 中文：FocusMAE：聚焦掩蔽自編碼器從超聲視頻中檢測膽囊癌
• Paper: https://arxiv.org/abs/2403.08848
• Code: https://github.com/sbasu276/FocusMAE

 

Federated Learning（聯(lián)邦學(xué)習(xí)） 

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

• 中文：選擇前請三思：帶有領(lǐng)域轉(zhuǎn)移的醫(yī)學(xué)圖像分析的聯(lián)邦證據(jù)主動學(xué)習(xí)
• Paper: https://arxiv.org/abs/2312.02567   

    

Medical Pre-training $ Foundation Model（預(yù)訓(xùn)練&基礎(chǔ)模型）

• VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis

• 中文：VoCo：一個簡單而有效的三維醫(yī)學(xué)圖像分析的體對比學(xué)習(xí)框架
• Paper: https://arxiv.org/abs/2402.17300
• Code: https://github.com/Luffy03/VoCo

• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning

• 中文：MLIP：使用發(fā)散編碼器和知識引導(dǎo)的對比學(xué)習(xí)增強醫(yī)學(xué)視覺表示
• Paper: https://arxiv.org/abs/2402.02045

• [Highlight!] Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

• 中文：持續(xù)自我監(jiān)督學(xué)習(xí)：走向通用的多模態(tài)醫(yī)學(xué)數(shù)據(jù)表示學(xué)習(xí)
• Paper:https://arxiv.org/abs/2311.17597
• Code: https://github.com/yeerwen/MedCoSS

• Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

• 中文：從X射線專家模型中提煉知識以啟動胸部CT圖像理解
• Paper: https://arxiv.org/abs/2404.04936v1

• Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

• Paper: https://arxiv.org/abs/2403.18271
• Code: https://github.com/Cccccczh404/H-SAM

• Low-Rank Knowledge Decomposition for Medical Foundation Models

• 中文：通過層次解碼釋放SAM在醫(yī)學(xué)適應(yīng)中的潛力
• Paper: https://arxiv.org/abs/2404.17184
• Code: https://github.com/MediaBrain-SJTU/LoRKD

 

Vision-Language Model (視覺-語言) 

• PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

• 中文：PairAug：增強的圖像文本對對放射學(xué)能做什么？
• Paper: https://arxiv.org/abs/2404.04960
• Code: https://github.com/YtongXie/PairAug   

• Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Matching Framework

• 中文：疾病描述分解以增強病理檢測：一個多方面視覺語言匹配框架
• Paper: https://arxiv.org/abs/2403.07636
• Code: https://github.com/HieuPhan33/MAVL

• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

• 中文：適應(yīng)視覺語言模型以在醫(yī)學(xué)圖像中實現(xiàn)泛化的異常檢測
• Paper: https://arxiv.org/abs/2403.12570
• Code: https://github.com/MediaBrain-SJTU/MVFA-AD

• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM

• 中文：OmniMedVQA：一個新的大規(guī)模全面評估基準(zhǔn)，針對醫(yī)學(xué)LVLM
• Paper: https://arxiv.org/abs/2402.09181

• CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification

• 中文：CARZero：放射學(xué)零樣本分類的交叉注意力對齊
• Paper: https://arxiv.org/abs/2402.17417

• FairCLIP: Harnessing Fairness in Vision-Language Learning.

• 中文：FairCLIP：在視覺語言學(xué)習(xí)中利用公平性
• Paper: https://arxiv.org/abs/2403.19949
• Code: https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP

 

Computational Pathology (計算病理)  

• Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

• 中文：具有精細(xì)視覺語義交互的可泛化全片圖像分類
• Paper: https://arxiv.org/abs/2402.19326

• Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

• 中文：特征再嵌入：朝著計算病理學(xué)的基礎(chǔ)模型級性能邁進(jìn)
• Paper: https://arxiv.org/abs/2402.17228
• Code: https://github.com/DearCaat/RRT-MIL

• PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

• 中文：PrPSeg：全景腎病病理分割的通用命題學(xué)習(xí)
• Paper: https://arxiv.org/abs/2402.19286

• ChAda-ViT: Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

• 中文：ChAda-ViT：通道自適應(yīng)注意力用于異質(zhì)顯微圖像的聯(lián)合表示學(xué)習(xí)
• Paper: https://arxiv.org/abs/2311.15264
• Code: https://github.com/nicoboou/chada_vit

• SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology    

• 中文：SI-MIL：馴服深度MIL以實現(xiàn)千兆像素組織病理學(xué)的自我解釋性
• Paper: https://arxiv.org/abs/2312.15010

• Transcriptomics-guided Slide Representation Learning in Computational Pathology.

• 中文：計算病理學(xué)中轉(zhuǎn)錄組學(xué)指導(dǎo)的切片表示學(xué)習(xí)
• Paper: https://arxiv.org/abs/2405.11618
• Code: https://github.com/mahmoodlab/TANGLE

 

Others

• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling

• 中文：看見未見：通過幾何約束的概率建模發(fā)現(xiàn)新型生物醫(yī)學(xué)概念
• Paper: https://arxiv.org/html/2403.01053v2   

      

   

    

     
      

       

      
      

       

        
          

           

            
             

              

               

                

                 

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