Pathology imaging is a key method in medical diagnostics, offering detailed information on tissue structures and biomarkers. However, traditional approaches to measuring biomarker intensity can be time-consuming, which is why new machine-learning methods are needed to predict these biomarkers directly from images. By identifying and analyzing biomarkers in this way, doctors can better predict patient grades, postoperative responses, and overall disease progression. This approach also presents significant commercial potential.
To address this issue, we adopted the Green U-Shaped Learning (GUSL) pipeline. Specifically, we extracted 15 randomly selected regions of interest (ROIs) from each pathology slide and resized them into patches. Each ROI was then analyzed to determine its biomarker density. GUSL utilizes Green Learning techniques like PixelHop, RFT, and LNT to integrate features from different spatial scales and refine predictions level by level. While this method shows promising results, it requires further adjustments to effectively handle ROIs with larger spatial sizes.
