Segmenting Potentially Cancerous Areas in Prostate Biopsies using SemiAutomatically Annotated Data. Gleason grading specified in ISUP 2014 is the clinical standard in staging prostate cancer and the most important part of the treatment decision. However, the grading is subjective and suffers from high intra and interuser variability. To improve the consistency and objectivity in the grading, we introduced glandular tissue WithOut Basal cells (WOB) as the ground truth. The presence of basal cells is the most accepted biomarker for benign glandular tissue and the absence of basal cells is a strong indicator of acinar prostatic adenocarcinoma, the most common form of prostate cancer. Glandular tissue can objectively be assessed as WOB or not WOB by using specific immunostaining for glandular tissue (Cytokeratin 818) and for basal cells (Cytokeratin 56  p63). Even more, WOB allowed us to develop a semiautomated data generation pipeline to speed up the tremendously time consuming and expensive process of annotating whole slide images by pathologists. We generated 295 prostatectomy images exhaustively annotated with WOB. Then we used our Deep Learning Framework, which achieved the 2nd best reported score in Camelyon17 Challenge, to train networks for segmenting WOB in needle biopsies. Evaluation of the model on 63 needle biopsies showed promising results which were improved further by finetuning the model on 118 biopsies annotated with WOB, achieving F1score of 0.80 and PrecisionRecall AUC of 0.89 at the pixellevel. Then we compared the performance of the model against 17 biopsies annotated independently by 3 pathologists using only Hu0026E staining. The comparison demonstrated that the model performed on a par with the pathologists. Finally, the model detected and accurately outlined existing WOB areas in two biopsies incorrectly annotated as totally WOBfree biopsies by three pathologists and in one biopsy by two pathologists.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.