Patch-Based Convolutional Neural Networks for TCGA-BRCA Breast Cancer Classification

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. T...

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Bibliographic Details
Main Authors: Villareal, Rosiel Jazmine T, Abu, Patricia Angela R
Format: text
Published: Archīum Ateneo 2021
Subjects:
Computer vision
Convolutional neural networks
Breast cancer classification
Computer Sciences
Databases and Information Systems
Oncology
Online Access:https://archium.ateneo.edu/discs-faculty-pubs/248
https://link.springer.com/chapter/10.1007/978-3-030-90436-4_3
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Institution: Ateneo De Manila University
Description
Summary: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.

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