Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks

Abstract

Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists’ review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46±0.40)% and identifies the DCIS tissue patches with a F1-score of (97.40±1.18)% (mean±variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.