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Histopathology artifact detection accuracy hinges on tissue detection methods

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the impact of tissue detection methods on the accuracy of diffusion-based histopathology artifact detectors. The study found that different tissue detection techniques significantly influence the false positive rates of these detectors, with entropy-based detection outperforming Otsu-based methods. The composition of the 'clean pool' used for training, specifically the inclusion of clear-space tissues, was identified as a key factor in false positives, rather than just the size of the pool. This research highlights the critical role of tissue detection in quality control for one-class artifact detection models in histopathology. AI

IMPACT Highlights how preprocessing choices in AI models can significantly impact diagnostic accuracy in medical imaging.

RANK_REASON Academic paper on a specific technical finding in AI/ML research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Histopathology artifact detection accuracy hinges on tissue detection methods

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Academic paper on a specific technical finding in AI/ML research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Konstantinos Moutselos, Ilias Maglogiannis ·

    Tissue Detection Determines False Positives in Diffusion-Based Histopathology Artifact Detection

    arXiv:2609.40083v1 Announce Type: cross Abstract: One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by a preprocessing pipeline whose tissue-detection step is usually treated as neutra…

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