摘要
Medical image analysis plays an irreplaceable role in diagnosing,treating,and monitoring various diseases.Convolutional neural networks(CNNs)have become popular as they can extract intricate features and patterns from extensive datasets.The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained models.The paper also discusses how transfer learning has been applied to different areas within medical image analysis.This comprehensive overview aims to assist researchers,clinicians,and policymakers by providing detailed insights,helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.
基金
Biotechnology and Biological Sciences Research Council,Grant/Award Number:RM32G0178B8
Royal Society,Grant/Award Number:RP202G0230
Fight for Sight UK,Grant/Award Number:24NN201
MRC,Grant/Award Number:MC_PC_17171
BHF,Grant/Award Number:AA/18/3/34220
Hope Foundation for Cancer Research,Grant/Award Number:RM60G0680
GCRF,Grant/Award Number:P202PF11
Sino-UK Industrial Fund,Grant/Award Number:RP202G0289
LIAS,Grant/Award Numbers:P202ED10,P202RE969
Data Science Enhancement Fund,Grant/Award Number:P202RE237
Sino-UK Education Fund,Grant/Award Number:OP202006。
作者简介
Correspondence:Yudong Zhang.Email:yudongzhang@seu.edu.cn,https://orcid.org/0000-0002-4870-1493。
