Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805–0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

Large-scale annotated image datasets like ImageNet and CIFAR-10 have been essential in developing and testing sophisticated new machine learning algorithms for natural vision tasks. Such datasets allow the development of neural networks to make visual discriminations that are done by humans in everyday * Respective contributions. 1. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/396663 doi: bioRxiv preprint first posted online Aug. 21, 2018; activities, e.g. discriminating classes of vehicles. An emerging field -computational pathology -applies such machine learning algorithms to the highly specialized vision task of diagnosing cancer or other diseases from pathology images. Importantly, labeling pathology images requires pathologists who have had decades of training, but due to the demands on pathologists' time (e.g. clinical service) obtaining a large annotated dataset of pathology images for supervised learning is difficult. To facilitate advances in computational pathology, on a scale similar to advances obtained in natural vision tasks using ImageNet, we leverage the power of social media. Pathologists worldwide share annotated pathology images on Twitter, which together provide thousands of diverse pathology images spanning many sub-disciplines. From Twitter, we assembled a dataset of 2,746 images from 1,576 tweets from 13 pathologists from 8 countries; each message includes both images and text commentary. To demonstrate the utility of these data for computational pathology, we apply machine learning to our new dataset to test whether we can accurately identify different stains and discriminate between different tissues. Using a Random Forest, we report (i) 0.959 ± 0.013 Area Under Receiver Operating Characteristic [AUROC] when identifying single-panel human hematoxylin and eosin [H&E] stained slides that are not overdrawn and (ii) 0.996 ± 0.004 AUROC when distinguishing H&E from immunohistochemistry [IHC] stained microscopy images. Moreover, we distinguish all pairs of breast, dermatological, gastrointestinal, genitourinary, and gynecological [gyn] pathology tissue types, with mean AUROC for any pairwise comparison ranging from 0.771 to 0.879. This range is 0.815 to 0.879 if gyn is excluded. We report 0.815 ± 0.054 AUROC when all five tissue types are considered in a single multiclass classification task. Our goal is to make this large-scale annotated dataset publicly available for researchers worldwide to develop, test, and compare their machine learning methods, an important step to advancing the field of computational pathology.