Medical papers in CVPR

CVPR 2023

  • Mingjie Li et. al., Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation. [pdf] [code]
  • Zhongzhen Huang et. al., KiUT: Knowledge-injected U-Transformer for Radiology Report Generation.
  • Zhanyu Wang et. al., METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens. [pdf]
  • Tim Tanida et. al., Interactive and Explainable Region-guided Radiology Report Generation. [pdf] [code]
  • Ziyun Yang et. al., Directional Connectivity-based Segmentation of Medical Images. [pdf] [code]
  • Yunhao Bai et. al., Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation.
  • Mingze Yuan et. al., Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization. [pdf]
  • Shruthi Bannur et. al., Learning to Exploit Temporal Structure for Biomedical Vision–Language Processing. [pdf]
  • Meirui Jiang et. al., Fair Federated Medical Image Segmentation via Client Contribution Estimation. [pdf]
  • Hritam Basak et. al., Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation.
  • Yuting He et. al., Geometric Visual Similarity Learning in 3D Medical Image Self-Supervised Pre-training. [pdf] [code]
  • AIMON RAHMAN et. al., Ambiguous Medical Image Segmentation using Diffusion Models. [pdf] [code]
  • Heng Cai et. al., Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation. [pdf] [code]
  • Cheng Jiang et. al., Hierarchical discriminative learning improves visual representations of biomedical microscopy. [pdf]
  • Yongchao Wang et. al., MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation.
  • Hyungseob Shin et. al., SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation.
  • Zeng Qingjie et. al., PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training.
  • Shiqi Huang et. al., Rethinking Few-Shot Medical Segmentation: A Vector Quantization View.

CVPR 2022

  • Mingjie Li et. al., Cross-Modal Clinical Graph Transformer for Ophthalmic Report Generation [pdf]
  • Chaowei Fang et. al., Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis. [pdf]
  • Fengbei Liu et. al., ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification. [pdf] [code]
  • Ke Zhang et. al., CycleMix: A Holistic Strategy for Medical Image Segmentation From Scribble Supervision. [pdf] [code]
  • Zhang Chen et. al., C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image. [pdf]
  • Ziqi Zhou et. al., Generalizable Cross-Modality Medical Image Segmentation via Style Augmentation and Dual Normalization. [pdf] [code]
  • Christos Matsoukas et. al., What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors. [pdf] [code]
  • Quan Quan et. al., Which Images To Label for Few-Shot Medical Landmark Detection?. [pdf]
  • Cheng Peng et. al., HyperSegNAS: Bridging One-Shot Neural Architecture Search With 3D Medical Image Segmentation Using HyperNet. [pdf]
  • Wenqiao Zhang et. al., BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation. [pdf] [code]
  • Tony C. W. Mok et. al., Affine Medical Image Registration With Coarse-To-Fine Vision Transformer. [pdf] [code]
  • Jinseong Jang et. al., M3T: Three-Dimensional Medical Image Classifier Using Multi-Plane and Multi-Slice Transformer. [pdf]
  • Jiancheng Yang et. al., ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging. [pdf]
  • Yucheng Tang et. al., Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis. [pdf] [code]
  • Aiham Taleb et. al., ContIG: Self-Supervised Multimodal Contrastive Learning for Medical Imaging With Genetics. [pdf]
  • Fatemeh Haghighi et. al., DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis. [pdf] [code]
  • Jianfeng Wang et. al., Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation. [pdf] [code]
  • Yu Feng et. al., FIBA: Frequency-Injection Based Backdoor Attack in Medical Image Analysis. [pdf] [code]
  • An Xu et. al., Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation. [pdf]