The Taub Faculty of Computer Science Events and Talks

The diagnosis and treatment planning of many diseases, such as cancer and auto-immune conditions, rely on histological slides. In recent years, digital pathology has become more abundant allowing high-thruput digitization of pathology images and the use of AI to analyze and interpret them. Yet, there are still inherent challenges in harnessing AI for pathology that includes coping with features in multiple size scales, the ability to achieve interpretability of the AI results, and biased datasets that impede the ability to produce reliable decision systems. In the first part of the talk, we will present our AI-based decision support system for the diagnosis of Eosinophilic esophagitis (EoE), a chronic immune disease that is second only to gastroesophageal reflux disease as the leading cause of chronic refractory dysphagia in adults and children. Diagnostics of EoE rely on counting single immune cells within a huge whole-slide image, a time-consuming process that is prone to errors. Our platform goes beyond recapturing the current manual histological gold standard by AI and reveals novel local and spatial biomarkers for EoE diagnosis, and can be harnessed to the diagnostics of other conditions. In the second part, we will present a novel approach for generating synthetic semantic masks of histological tissues. Many histology datasets are biased due to biological factors (many healthy patients and many very sick but not enough around the decision threshold) or technical factors (images from a particular device or a specific campus). While GAN-based solutions can produce realistic textures, their ability to recapitulate the spatial distribution of features in tissues is limited. One solution is to use image translation conditional networks, however, generating proper conditional masks of tissues, as input to the network, is still not done successfully. We will present our approach that can produce realistic semantic masks of various organs such as lungs and skin. This allows the generation of synthetic histology slides by controlling their spatial distribution, thus providing unbiased datasets that can facilitate the development and testing of AI pathology solutions.