Deep Learning in Automated Breast Cancer Diagnosis by Learning the Breast Histology from Microscopy

Breast cancer is one of the most common cancers in women. With early diagnosis, some breast cancers are highly curable. However, the concordance rate of breast cancer diagnosis from histology slides by pathologists is unacceptably low. Classifying normal versus tumor breast tissues from breast histology microscopy images is an ideal case to use for deep learning and could help to more reproducibly diagnose breast cancer. This webinar will discuss using 42 combinations of deep learning models, image data preprocessing techniques, and hyperparameter configurations, with accuracy testing of tumor versus normal classification using the Breast Cancer Histology (BACH) dataset. Results of this process will be shared to demonstrate preprocessing and hyperparameter configurations have a direct impact on the performance of deep neural networks for image classification.

Presented by Qiangqiang Gu, MS, PhD Candidate, University of Minnesota