Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
This work addresses breast cancer diagnosis for medical professionals, but it appears incremental as it applies existing methods to a new dataset.
The paper tackled breast cancer histology image classification and whole slide image segmentation using densely connected convolutional networks, achieving results on the BACH dataset but without specific numbers provided.
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).