78 citations · 185 across the 13 of their papers we have counts for
7 papers · 1 filter
Improving Medical Image Generative Models with Fréchet Distance Loss
Andrew Marshall, Xuanang Xu, Xiaoran Zhang +3
Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture complex morphological charac…
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
Xiangyi Yan, Junayed Naushad, Chenyu You +6
Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in…
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Chenyu You, Weicheng Dai, Yifei Min +2
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep lear…
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Chenyu You, Weicheng Dai, Yifei Min +3
Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or r…
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min +5
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pa…
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Chenyu You, Jinlin Xiang, Kun Su +5
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model th…
Class-Aware Adversarial Transformers for Medical Image Segmentation
Chenyu You, Ruihan Zhao, Fenglin Liu +5
Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain. However, current transformer-based models suffer from…