FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation

no code implementations • 7 Nov 2024 • Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin, Shaoliang Peng

Training a centralized model directly is challenging to implement in medical settings due to these privacy concerns. This paper addresses the dispersed nature and privacy sensitivity of medical image data by employing a federated learning framework, allowing medical institutions to collaboratively learn while protecting patient privacy.

Federated Learning Image Segmentation +3

MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results

no code implementations • 11 Jun 2024 • Xin Jin, Chunle Guo, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Yuekun Dai, Peiqing Yang, Chen Change Loy, Ruoqi Li, Chang Liu, Ziyi Wang, Yao Du, Jingjing Yang, Long Bao, Heng Sun, Xiangyu Kong, Xiaoxia Xing, Jinlong Wu, Yuanyang Xue, Hyunhee Park, Sejun Song, Changho Kim, Jingfan Tan, Wenhan Luo, Zikun Liu, Mingde Qiao, Junjun Jiang, Kui Jiang, Yao Xiao, Chuyang Sun, Jinhui Hu, Weijian Ruan, Yubo Dong, Kai Chen, Hyejeong Jo, Jiahao Qin, Bingjie Han, Pinle Qin, Rui Chai, Pengyuan Wang

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.

Image Denoising