Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data - 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data - 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
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医学图像计算中机器智能的解释性、拓扑数据分析及其在医学数据中的应用 - 第四届国际研讨会，iMIMIC 2021，和第一届国际研讨会，TDA4MedicalData 2021，与 MICCAI 2021 联合举行，法国斯特拉斯堡，2021 年 9 月 27 日
DOI：
10.1007/978-3-030-87444-5_6
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Baltatzis V
中科院分区：
文献类型：
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作者：
Baltatzis V
卷积神经网络(CNN)被广泛应用于包括医学成像在内的各种领域的图像分类。虽然大多数研究在这类任务中使用交叉熵作为损失函数，但越来越多的方法已经转向一系列基于对比学习的损失。尽管人们经常使用准确度、敏感度和特异度等性能指标来评估CNN分类器，但这些分类器实际学习的特征很少被识别，并且它们对非分布测试样本的分类性能的影响也没有得到充分的研究。在本文中，受肺结节分类这一现实世界任务的启发，我们研究了CNN在具有受控变异模式的合成数据集的不同分布上训练和测试时所学习的特征。研究表明，不同的损失函数会导致不同的特征被学习，从而影响分类器对未知数据的泛化能力。这项研究为医学成像任务的深度学习解决方案的设计提供了一些重要的见解。
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss function in such tasks, a growing number of approaches have turned to a family of contrastive learning-based losses. Even though performance metrics such as accuracy, sensitivity and specificity are regularly used for the evaluation of CNN classifiers, the features that these classifiers actually learn are rarely identified and their effect on the classification performance on out-of-distribution test samples is insufficiently explored. In this paper, motivated by the real-world task of lung nodule classification, we investigate the features that a CNN learns when trained and tested on different distributions of a synthetic dataset with controlled modes of variation. We show that different loss functions lead to different features being learned and consequently affect the generalization ability of the classifier on unseen data. This study provides some important insights into the design of deep learning solutions for medical imaging tasks.