Progressive expansion: Cost-efficient medical image analysis model with reversed once-for-all network training paradigm
Document Type
Article
Publication Date
5-1-2024
Abstract
Low computational cost artificial intelligence (AI) models are vital in promoting the accessibility of real-time medical services in underdeveloped areas. The recent Once -For -All (OFA) network (without retraining) can directly produce a set of sub -network designs with Progressive Shrinking (PS) algorithm; however, the training resource and time inefficiency downfalls are apparent in this method. In this paper, we propose a new OFA training algorithm, namely the Progressive Expansion (ProX) to train the medical image analysis model. It is a reversed paradigm to PS, where technically we train the OFA network from the minimum configuration and gradually expand the training to support larger configurations. Empirical results showed that the proposed paradigm could reduce training time up to 68%; while still being able to produce sub -networks that have either similar or better accuracy compared to those trained with OFA-PS on ROCT (classification), BRATS and Hippocampus (3D -segmentation) public medical datasets. The code implementation for this paper is accessible at: https://github.com/shin-wl/ProX-OFA.
Keywords
Medical image analysis, Machine learning, Model optimization, Cost-effective model
Divisions
fsktm
Funders
MyIndustry AI Scholarship Programme - Intel Corporation, Malaysia, Universiti Malaya, Malaysia,Malaysia Digital Economy Corporation (MDEC)
Publication Title
Neurocomputing
Volume
581
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS