1 citations · 1 across the 7 of their papers we have counts for
7 papers
Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency
Wenhan Jiang, Zhipeng Deng, Jiale Zhou +3
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition d…
VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation
Zhipeng Deng, Jiale Zhou, Wenhan Jiang +4
Deploying multi-sequence magnetic resonance imaging (MRI) segmentation models to new clinical environments is challenging due to variations in scanners and acquisition protocols. A…
VoxShield: Protecting 3D Medical Datasets from Unauthorized Training via Frequency-Aware Inter-Slice Disruption
Xinyao Liu, Zhipeng Deng, Wenhan Jiang +4
The release of public 3D medical image segmentation (MIS) datasets accelerates clinical research but simultaneously heightens risks of unauthorized AI model training. While Unlearn…
Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering
Lin Fan, Yafei Ou, Zhipeng Deng +8
Chain-of-thought (CoT) reasoning has advanced medical visual question answering (VQA), yet most existing CoT rationales are free-form and fail to capture the structured reasoning p…
Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
Lin Fan, Pengyu Dai, Zhipeng Deng +4
Clinical image interpretation is inherently multi-step and tool-centric: clinicians iteratively combine visual evidence with patient context, quantify findings, and refine their de…
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation
Zhipeng Deng, Zhe Xu, Tsuyoshi Isshiki +1
Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in federated semi-supervised lear…
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification
Zhipeng Deng, Yuqiao Yang, Kenji Suzuki
Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Current FL research focuses on communication efficiency, privacy p…