A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images - Farajzadeh, Nacer; Sadeghzadeh, Nima; Hashemzadeh, Mahdi (Computers in biology and medicine, 2022)
Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoder-decoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors. •A novel neural network is proposed to mask nuclei with 98.61% accuracy.•A post-processing procedure is offered to balance the network's output image.•The model in this study generalizes to a partially annotated dataset.•Augments data and improves accuracy by dividing histopathology images into blocks.•Endorses that a custom model instead of transfer learning is better in this realm.