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Digital whole slide imaging has the potential to change diagnostic pathology by enabling the use of computeraided diagnosis systems. To this end, we used a dataset of 240 digital slides that are interpreted and diagnosed by an expert panel to develop and evaluate image features for diagnostic classification of breast biopsy whole slides to four categories: benign, atypia, ductal carcinoma in-situ and invasive carcinoma. Starting with a tissue labeling step, we developed features that describe the tissue composition of the image and the structural changes. In this paper, we first introduce two models for the semantic segmentation of the regions of interest into tissue labels: an SVM-based model and a CNN-based model. Then, we define an image feature that consists of superpixel tissue label frequency and co-occurrence histograms based on the tissue label segmentations. Finally, we use our features in two diagnostic classification schemes: a four-class classification, and an alternative classification that is one-diagnosis-at-a-time starting with invasive versus benign and ending with atypia versus ductal carcinoma in-situ (DCIS). We show that our features achieve competitive results compared to human performance on the same dataset. Especially at the critical atypia vs. DCIS threshold, our system outperforms pathologists by achieving an 83% accuracy.

Automated Diagnosis of Breast Cancer and Pre-invasive Lesions on Digital Whole Slide Images