Application · Meta-learning through unrolled training
Gastric SLDD
Soft-Label Anonymous Gastric X-ray Image Distillation
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
ICIP 2020 · first public 2021-04-07 · arXiv 2104.02857
In one paragraph
Applies gradient-descent dataset distillation, jointly optimizing distilled images, distilled soft labels and the learning rate, to gastric X-ray images so the resulting tiny distilled set both compresses the dataset and anonymizes the original patient images by construction. Reports the compressed images no longer carry identifiable patient information while remaining useful for training.
Where it sits
- Meta-learning through unrolled training (Bi-level performance matching)
- Setting: Image classification
Design choices
| Labels | learned |
Abstract (verbatim from arXiv)
This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.
BibTeX (generated; prefer the venue's official entry)
@article{li2021soft,
title = {Soft-Label Anonymous Gastric X-ray Image Distillation},
author = {Guang Li and Ren Togo and Takahiro Ogawa and Miki Haseyama},
journal = {ICIP 2020},
year = {2021}
}Nearby in Meta-learning through unrolled training
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