The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

TL;DR

This study investigates how different loss functions influence the features learned by CNNs and their ability to generalize on out-of-distribution synthetic lung nodule data, providing insights for medical imaging applications.

Contribution

It offers an empirical analysis of the impact of various loss functions on feature learning and generalization in CNNs for lung nodule classification.

Findings

Different loss functions lead to distinct feature representations.

Loss choice significantly affects out-of-distribution generalization.

Contrastive losses may improve robustness on unseen data.

Abstract

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.