10 papers
High-Fidelity Medical Shape Generation via Skeletal Latent Diffusion
Guoqing Zhang, Jingyun Yang, Siqi Chen +2
Anatomy shape modeling is a fundamental problem in medical data analysis. However, the geometric complexity and topological variability of anatomical structures pose significant ch…
Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning
Weijia Feng, Jingyu Yang, Ruojia Zhang +7
Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical moni…
Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi-modal Gross Tumor Volume Segmentation
Jingyun Yang, Guoqing Zhang, Jingge Wang +1
Accurate gross tumor volume segmentation on multi-modal medical data is critical for radiotherapy planning in nasopharyngeal carcinoma and glioblastoma. Recent advances in deep neu…
Understanding Knowledge Transferability for Transfer Learning: A Survey
Haohua Wang, Jingge Wang, Zijie Zhao +9
Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This a…
Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Guoqing Zhang, Jingyun Yang, Yang Li
Deep learning-based point cloud modeling has been widely investigated as an indispensable component of general shape analysis. Recently, transformer and state space model (SSM) hav…
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation
Shutong Duan, Jingyun Yang, Yang Tan +3
How to mitigate negative transfer in transfer learning is a long-standing and challenging issue, especially in the application of medical image segmentation. Existing methods for r…
Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially
Jingyun Yang, Guoqing Zhang, Jingge Wang +1
Recent advances in foundation models have brought promising results in computer vision, including medical image segmentation. Fine-tuning foundation models on specific low-resource…
Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data
Jingyun Yang, Jingge Wang, Guoqing Zhang +1
The medical image processing field often encounters the critical issue of scarce annotated data. Transfer learning has emerged as a solution, yet how to select an adequate source t…
Flemme: A Flexible and Modular Learning Platform for Medical Images
Guoqing Zhang, Jingyun Yang, Yang Li
As the rapid development of computer vision and the emergence of powerful network backbones and architectures, the application of deep learning in medical imaging has become increa…
Finding the Most Transferable Tasks for Brain Image Segmentation
Yicong Li, Yang Tan, Jingyun Yang +2
Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source task…