Slide Screening of Metastases in Lymph Nodes via Conditional, Fully Convolutional Segmentation

We assess the viability of applying a conditional algorithm to the segmentation of Whole Slide Images (WSI) for human histopathology. Our objective is designing a deep network for automatic screening of large sets sentinel lymph-nodes of WSIs to detect those worth inspecting by a pathologist. Ideally, such system should modify and correct its behavior based on a limited set of examples, to foster interactivity and the incremental tuning to specific diagnostic pipelines and clinical practices and, not the least, to alleviate the task of collecting a suitable annotated dataset for training. In contrast, ‘classical’ supervised techniques require a vast dataset upfront and their behavior cannot be adapted unless through extensive retraining. The approach presented here is based on conditional and fully convolutional networks, which can segment a query image by conditioning on a support set of sparsely annotated images, fed at inference time. We describe the target scenario, the architecture used, and we present some preliminary results of segmentation experiments conducted on the publicly-available Camelyon16 dataset.