MixPatch: A New Method for Training Histopathology Image Classifiers

• Published in: Diagnostics (Basel) 2022-06, Vol.12 (6), p.1493
• Main Authors: Park, Youngjin, Kim, Mujin, Ashraf, Murtaza, Ko, Young Sin, Yi, Mun Yong
• Format: Article
• Language: English
• Subjects: Cancer; Classification; confidence calibration; Datasets; Deep learning; Design; Histopathology; histopathology image analysis; Human error; Methods; Morphology; Pathology; prediction uncertainty
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12061493

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.