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#digital-pathology News & Analysis

4 articles tagged with #digital-pathology. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles
AIBullisharXiv – CS AI · Jun 27/10
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CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval

CRISP is an unsupervised machine learning framework that automates the analysis of multiple whole-slide images (WSIs) in digital pathology by selectively sampling informative patches across all slides in a case rather than relying on a single pathologist-selected slide. The approach matches or exceeds current clinical practice for breast cancer diagnosis and retrieval while eliminating subjective slide selection and reducing computational burden.

AIBullisharXiv – CS AI · Feb 277/104
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Beyond the Monitor: Mixed Reality Visualization and Multimodal AI for Enhanced Digital Pathology Workflow

Researchers developed PathVis, a mixed-reality platform for Apple Vision Pro that revolutionizes digital pathology by allowing pathologists to examine gigapixel cancer diagnostic images through immersive visualization and multimodal AI assistance. The system replaces traditional 2D monitor limitations with natural interactions using eye gaze, hand gestures, and voice commands, integrated with AI agents for computer-aided diagnosis.

AINeutralarXiv – CS AI · Jun 86/10
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Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

The MIDOG 2025 challenge evaluated automated mitosis detection across 365 diverse tumor cases spanning 12 different human, canine, and feline types to assess real-world clinical applicability. Results showed top F1 scores of 0.740 for detection and 0.908 balanced accuracy for atypical mitotic figure classification, but revealed significant performance degradation in challenging tissue areas where false positives tripled, highlighting major limitations in current AI architectures.