Brief

Researchers have developed HemExp, a novel latent diffusion model designed to predict hematoma expansion after spontaneous intracerebral hemorrhage. This model generates patient-specific follow-up non-contrast CT images and hemorrhage segmentations, conditioned on baseline imaging and clinical data. By simulating realistic clinical scenarios and estimating distributions of plausible follow-up hematoma volumes, HemExp aims to support uncertainty-aware decision-making in neurosurgical care. AI

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