Patch-Based Convolutional Neural Networks for TCGA-BRCA Breast Cancer Classification
The current study automatically identified regions of interest and classified breast tumors in whole slide images from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) using patch-based convolutional neural networks (CNNs). Pre-processing techniques were applied on whole slide images. Then, whole slide images were tiled into patches, and patches containing regions of interest (ROIs) like nuclei-rich areas were identified. Afterwards, features from patches containing ROIs were extracted using CNNs and used to train patch-level classifiers. Finally, patch-level predictions were aggregated into slide-level predictions. Classification metrics like accuracy, precision, recall, and f1-score were used to evaluate results.
Villareal, R. J. T., & Abu, P. A. R. (2022). Patch-based convolutional neural networks for TCGA-BRCA breast cancer classification. In G. Bebis, V. Athitsos, T. Yan, M. Lau, F. Li, C. Shi, X. Yuan, C. Mousas, & G. Bruder (Eds.), Advances in Visual Computing (pp. 29–40). Springer International Publishing. https://doi.org/10.1007/978-3-030-90436-4_3