Image distillation for safe data sharing in histopathology
OA Location
Author(s)
Li, Zhe
Kainz, Bernhard
Type
Chapter
Abstract
Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availability and concerns about data sharing and privacy. Federated learning has addressed this challenge by training models locally and updating parameters on a server. However, issues, such as domain shift and bias, persist and impact overall performance. Dataset distillation presents an alternative approach to overcoming these challenges. It involves creating a small synthetic dataset that encapsulates essential information, which can be shared without constraints. At present, this paradigm is not practicable as current distillation approaches only generate non human readable representations and exhibit insufficient performance for downstream learning tasks. We train a latent diffusion model and construct a new distilled synthetic dataset with a small number of human readable synthetic images. Selection of maximally informative synthetic images is done via graph community analysis of the representation space. We compare downstream classification models trained on our synthetic distillation data to models trained on real data and reach performances suitable for practical application. Codes are available at https://github.com/ZheLi2020/InfoDist.
Editor(s)
Linguraru, MG
Dou, Q
Feragen, A
Giannarou, S
Glocker, B
Lekadir, K
Schnabel, JA
Date Issued
2024-10-03
Citation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024, 2024, 15010, pp.459-469
ISBN
978-3-031-72116-8
Publisher
Springer Nature Switzerland AG
Start Page
459
End Page
469
Journal / Book Title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Lecture Notes in Computer Science
Volume
15010
Copyright Statement
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Dataset Distillation
Image Generation
Life Sciences & Biomedicine
Privacy
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Technology
Publication Status
Published
Date Publish Online
2024-10-03