HSQ-VLM: A Novel Spatially-Constrained Quadrant Segmentation VLM Model for Explainability in Diabetic Retinopathy
Researchers have developed HSQ-VLM, a new vision-language model designed to improve the explainability of AI diagnostics for diabetic retinopathy. This model uses a novel quadrant segmentation pipeline with Landmark-Anchored Cartesian Cross-Attention and Topological Latent Partitioning to align retinal features with a fovea-centered coordinate system. The HSQ-VLM generates precise natural language reports by quantifying pathology with anatomical accuracy, achieving high sensitivity in detecting hemorrhages and microaneurysms on a dataset of 3,500 fundus images. AI
IMPACT This research offers a path toward more interpretable AI diagnostics in healthcare, potentially increasing trust and adoption of AI in clinical settings for conditions like diabetic retinopathy.