Deep neural networks for automated classification of colorectal polyps on histopathology slides: A multi-institutional evaluation

Histological classification of colorectal polyps plays a critical role in both screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathology slides could benefit clinicians and patients. Evaluate the performance and assess the generalizability of a deep neural network for colorectal polyp classification on histopathology slide images using a multi-institutional dataset. In this study, we developed a deep neural network for classification of four major colorectal polyp types, tubular adenoma, tubulovillous/villous adenoma, hyperplastic polyp, and sessile serrated adenoma, based on digitized histopathology slides from our institution, Dartmouth-Hitchcock Medical Center (DHMC), in New Hampshire. We evaluated the deep neural network on an internal dataset of 157 histopathology slide images from DHMC, as well as on an external dataset of 238 histopathology slide images from 24 different institutions spanning 13 states in the United States. We measured accuracy, sensitivity, and specificity of our model in this evaluation and compared its performance to local pathologists diagnoses at the point-of-care retrieved from corresponding pathology laboratories. For the internal evaluation, the deep neural network had a mean accuracy of 93.5% (95% CI 89.6%-97.4%), compared with local pathologists accuracy of 91.4% (95% CI 87.0%-95.8%). On the external test set, the deep neural network achieved an accuracy of 87.0% (95% CI 82.7%-91.3%), comparable with local pathologists accuracy of 86.6% (95% CI 82.3%-90.9%). If confirmed in clinical settings, our model could assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings.

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide.

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.