Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks

TL;DR

This paper explores the use of densely connected convolutional neural networks for classifying breast cancer histology images and segmenting whole slide images, aiming to improve computer-aided diagnosis accuracy.

Contribution

It investigates the application of densely connected CNNs and transfer learning approaches to breast cancer histology image analysis, advancing diagnostic methods.

Findings

Effective transfer learning strategies identified

Improved classification accuracy demonstrated

Potential for enhanced computer-aided diagnosis

Abstract

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).