5 papers · 1 filter
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…