Title: Identify Metastatic Tissue in Histopathological Biopsy Images Using Ensemble of Deep Learning Networks
Authors: Belal A . M. Alashqar, Samy S. Abu-Naser
Volume: 4
Issue: 3
Pages: 53-59
Publication Date: 2020/03/28//
Abstract:
Breast cancer is the most common invasive cancer in women and the second leading cause of cancer death in women after lung cancer. Early diagnosis of this type of cancer is critical for treatment and patient care. It has been shown that deep learning algorithms could identify metastases in SLN slides with 100% sensitivity, whereas 40% of the slides without metastases could be identified as such. In this study, we proposed an ensemble deep learning-based approach for automatic binary classification of breast histology images. The proposed ensemble model adapts two pre-trained CNNs, namely VGG16 and DenseNet121 and one custom CNN. The proposed method is validated on a slightly modified version of the PatchCamelyon (PCam) dataset. The proposed method achieved an accuracy of 96.5% on a held-out test set, demonstrating the feasibility of this approach.