Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.

ABSTRACT: Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN

SUBMITTER: Voon W 

PROVIDER: S-EPMC10665422 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature