Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks.

ABSTRACT: In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilities toward the majority class. Calibration has been proposed to alleviate some of these effects. However, there is insufficient analysis explaining whether and when calibrating a model would be beneficial. In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones. For this, we study the following variations: (i) the degree of imbala

SUBMITTER: Rajaraman S 

PROVIDER: S-EPMC8794113 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature