16 citations · 37 across the 12 of their papers we have counts for
12 papers
Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation
Chun-Mei Feng, Yuanyang He, Jian Zou +6
Existing test-time prompt tuning (TPT) methods focus on single-modality data, primarily enhancing images and using confidence ratings to filter out inaccurate images. However, whil…
Class Balance Matters to Active Class-Incremental Learning
Zitong Huang, Ze Chen, Yuanze Li +6
Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li +5
Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the…
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection
Yuncheng Jiang, Zixun Zhang, Jun Wei +5
AI-assisted lesion detection models play a crucial role in the early screening of cancer. However, previous image-based models ignore the inter-frame contextual information present…
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
Yuncheng Jiang, Yiwen Hu, Zixun Zhang +7
Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the la…