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Progressive expansion: Cost-efficient medical image analysis model with reversed once-for-all network training paradigm.

Authors :
Lim, Shin Wei
Chan, Chee Seng
Mohd Faizal, Erma Rahayu
Ewe, Kok Howg
Source :
Neurocomputing. May2024, Vol. 581, pN.PAG-N.PAG. 1p.
Publication Year :
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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09252312
Volume :
581
Database :
Academic Search Index
Journal :
Neurocomputing
Publication Type :
Academic Journal
Accession number :
176356349
Full Text :
https://doi.org/10.1016/j.neucom.2024.127512