The Context-Guided Segmentation Network (CGS-Net) developed by University of Maine researchers introduces a deep learning architecture designed to interpret microscopic images of tissue with greater precision than conventional AI models. Powered by a dual-encoder model that mirrors the workflow of a pathologist examining a slide, one branch of the network processes a high-resolution image patch to capture cell-level details, while the other examines a lower-resolution patch encompassing the surrounding tissue. A system of interconnected encoders and decoders uses data from both the high and low resolution images for a complete analysis.