# Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks

Source: [Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks](<https://www.sota2.com/research/paper/68fa64481e209131cea03ace>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:00:11.434Z

Authors: Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust

Published: 2018-04-09T19:40:02.000Z

## 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).

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.04595>)

[PDF](<https://arxiv.org/pdf/1804.04595.pdf>)