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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

    Researchers have developed Atlas H&E-TME, an AI system designed to analyze Hematoxylin and eosin (H&E) stained whole-slide images in histopathology. This system can predict tissue quality, region, and cell type labels, generating over 4,500 quantitative readouts per slide at a cellular level. It achieves accuracy comparable to expert pathologists by using a novel dual validation framework that combines an IHC-informed consensus protocol with extensive benchmarking on over 200,000 pathologist annotations across diverse cancer types and scanner models. AI

    IMPACT Enhances quantitative analysis of pathology slides, potentially improving diagnostic accuracy and biomarker discovery.