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TRV-2026-0252Certified recordPeer-reviewed

A deep learning framework for efficient pathology image analysis

Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature…

Health · G Space — documented gain · certified 2026-07-17 · v1 · article view · machine-readable

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EAGLE selectively analyzes informative regions of whole-slide pathology images, improving classification accuracy by up to 23% and reducing per-slide processing to 2.27 seconds.

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Truvace Impact Record TRV-2026-0252, v1: “A deep learning framework for efficient pathology image analysis.” Truvace, 2026-07-17. /record/TRV-2026-0252 (accessed at citation time). sha256 72a04d43602da28d…

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