78 citations · 185 across the 13 of their papers we have counts for
13 papers
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…
Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation
Jiyao Wang, Nicha C. Dvornek, Lawrence H. Staib +1
Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging d…
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…
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Kun Han, Yifeng Xiong, Chenyu You +5
Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical imag…
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…
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Shixing Cao, Nicholas Konz, James Duncan +1
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is…
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Chenyu You, Weicheng Dai, Fenglin Liu +6
Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly fo…
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Xiaoran Zhang, Chenyu You, Shawn Ahn +3
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explic…
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…
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness
Siyuan Dong, Gilbert Hangel, Wolfgang Bogner +7
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial reso…
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…