Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yaoyang Wu, Simon Fong, Liansheng Liu
{"title":"Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system","authors":"Yaoyang Wu,&nbsp;Simon Fong,&nbsp;Liansheng Liu","doi":"10.1007/s10489-024-05916-x","DOIUrl":null,"url":null,"abstract":"<p>Numerous applications have explored medical image classification using deep learning models. With the emergence of Explainable AI (XAI), researchers have begun to recognize its potential in validating the authenticity and correctness of results produced by black-box deep learning models. On the other hand, current diagnostic approaches for osteonecrosis face significant challenges, including difficulty in early detection, subjectivity in image interpretation, and reliance on surgical interventions without a comprehensive diagnostic foundation. This paper presents a novel Medical Computer-Aid-Diagnosis System—the Shadow Learning System framework—which integrates a convolutional neural network (CNN) with an Explainable AI method. This system not only performs conventional computer-aiding-diagnosis functions but also uniquely exploits misclassified data samples to provide additional medically relevant information from the machine learning model’s perspective, assisting doctors in their diagnostic process. The implementation of XAI techniques in our proposed system goes beyond merely validating CNN model results; it also enables the extraction of valuable information from medical images through an unconventional machine learning perspective. Our paper aims to enhance and extend the general structure and detailed design of the Shadow Learner System, making it more advantageous not only for human users but also for the deep learning model itself. A case study on femoral head osteonecrosis was conducted using our proposed system, which demonstrated improved accuracy and reliability in its prediction results. Experimental results interpreted using XAI methods are visualized to prove the confidence of our proposed model that generates reasonable results, confirming the effectiveness of the proposed model.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-024-05916-x","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Numerous applications have explored medical image classification using deep learning models. With the emergence of Explainable AI (XAI), researchers have begun to recognize its potential in validating the authenticity and correctness of results produced by black-box deep learning models. On the other hand, current diagnostic approaches for osteonecrosis face significant challenges, including difficulty in early detection, subjectivity in image interpretation, and reliance on surgical interventions without a comprehensive diagnostic foundation. This paper presents a novel Medical Computer-Aid-Diagnosis System—the Shadow Learning System framework—which integrates a convolutional neural network (CNN) with an Explainable AI method. This system not only performs conventional computer-aiding-diagnosis functions but also uniquely exploits misclassified data samples to provide additional medically relevant information from the machine learning model’s perspective, assisting doctors in their diagnostic process. The implementation of XAI techniques in our proposed system goes beyond merely validating CNN model results; it also enables the extraction of valuable information from medical images through an unconventional machine learning perspective. Our paper aims to enhance and extend the general structure and detailed design of the Shadow Learner System, making it more advantageous not only for human users but also for the deep learning model itself. A case study on femoral head osteonecrosis was conducted using our proposed system, which demonstrated improved accuracy and reliability in its prediction results. Experimental results interpreted using XAI methods are visualized to prove the confidence of our proposed model that generates reasonable results, confirming the effectiveness of the proposed model.

Abstract Image

增强医学图像分类的可解释性及利用阴影学习系统分析骨坏死x线图像
许多应用已经探索了使用深度学习模型的医学图像分类。随着可解释人工智能(XAI)的出现,研究人员已经开始认识到它在验证黑箱深度学习模型产生的结果的真实性和正确性方面的潜力。另一方面,目前的骨坏死诊断方法面临着重大挑战,包括早期发现困难,图像解释的主观性,以及在没有全面诊断基础的情况下依赖手术干预。本文提出了一种新的医学计算机辅助诊断系统——影子学习系统框架,该框架将卷积神经网络(CNN)与可解释的人工智能方法相结合。该系统不仅执行传统的计算机辅助诊断功能,而且还独特地利用错误分类的数据样本,从机器学习模型的角度提供额外的医学相关信息,协助医生进行诊断过程。在我们提出的系统中实现XAI技术不仅仅是验证CNN模型结果;它还可以通过非常规的机器学习角度从医学图像中提取有价值的信息。我们的论文旨在增强和扩展影子学习系统的总体结构和详细设计,使其不仅对人类用户更有利,而且对深度学习模型本身也更有利。应用该系统对股骨头坏死病例进行了研究,结果表明该系统预测结果的准确性和可靠性有所提高。利用XAI方法解释的实验结果可视化,证明了我们所提出的模型的置信度,产生了合理的结果,证实了所提出模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信