Work

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

Year: 2024

Type: conference-paper

Abstract: Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning, and post-operative assessment. With the rapid advancement of deep learning technologies, the development of numerous models based on convolutional neural networks and transformer-based models has been applied to the precise segmentation of WSIs. However, due to privacy regulations and the need to protect patient confidentiality, centralized storage and processing of image data are impractical. 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. Additionally, to address the issue of original data reconstruction through gradient inversion during the federated learning training process, differential privacy introduces noise into the model updates, preventing attackers from inferring the contributions of individual samples, thereby protecting the privacy of the training data.Experimental results show that the proposed method, FedDP, minimally impacts model accuracy while effectively safeguarding the privacy of cancer pathology image data, with only a slight decrease in Dice, Jaccard, and Acc indices by 0.55%, 0.63%, and 0.42%, respectively. This approach facilitates cross-institutional collaboration and knowledge sharing while protecting sensitive data privacy, providing a viable solution for further research and application in the medical field.

Source: IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

DOI: 10.1109/bibm62325.2024.10822021

Authors: Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin, Shaoliang Peng

Institutions: Hunan University, Central South University, North University of China

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green