1 citations · 1 across the 1 of their papers we have counts for
6 papers
Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
Zhuodi Cai, Ziyu Xu, Juan Pampin
We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classificat…
OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models
Ningyong Wu, Jiangbo Zhang, Wenhong Zhao +6
The growing volume of medical imaging data has increased the need for automated diagnostic tools, especially for musculoskeletal injuries like rib fractures, commonly detected via…
Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Hongyi Pan, Ziliang Hong, Gorkem Durak +2
Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However, its performance is often limited by s…
Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis
Cheng Yuan, Jian Jiang, Kunyi Yang +11
Surgical video segmentation is critical for AI to interpret spatial-temporal dynamics in surgery, yet model performance is constrained by limited annotated data. The SAM2 model, pr…
IPMN Risk Assessment under Federated Learning Paradigm
Hongyi Pan, Ziliang Hong, Gorkem Durak +17
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop…
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Hongyi Pan, Gorkem Durak, Zheyuan Zhang +16
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However…