Evidence-empowered Transfer Learning for Alzheimer's Disease

Autor:	Ong, Kai Tzu-iunn, Kim, Hana, Kim, Minjin, Jang, Jinseong, Sohn, Beomseok, Choi, Yoon Seong, Hwang, Dosik, Hwang, Seong Jae, Yeo, Jinyoung
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Transfer learning has been widely utilized to mitigate the data scarcity problem in the field of Alzheimer's disease (AD). Conventional transfer learning relies on re-using models trained on AD-irrelevant tasks such as natural image classification. However, it often leads to negative transfer due to the discrepancy between the non-medical source and target medical domains. To address this, we present evidence-empowered transfer learning for AD diagnosis. Unlike conventional approaches, we leverage an AD-relevant auxiliary task, namely morphological change prediction, without requiring additional MRI data. In this auxiliary task, the diagnosis model learns the evidential and transferable knowledge from morphological features in MRI scans. Experimental results demonstrate that our framework is not only effective in improving detection performance regardless of model capacity, but also more data-efficient and faithful. Comment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2023. The authorship was changed from co-first authors to a single first author, which was authorized by the adviser/corresponding author Jinyoung Yeo (Apr 18th, 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.01105 Zobrazit plný text záznamu View this record from Arxiv