Comparison of an Attention-Based Multiple Instance Learning (MIL) With a Visual Transformer Model: Two Weakly Supervised Deep Learning (DL) Algorithms for the Detection of Histopathologic Lesions in the Rat Liver to Distinguish Normal From Abnormal

The histopathologic evaluation of regulatory toxicity studies using artificial intelligence (AI) has the potential to increase study efficiency. For example, AI could initially identify and exclude all organs without histopathologic lesions, allowing pathologists to focus solely on evaluating organs...

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Veröffentlicht in:	Toxicologic pathology Jg. 53; H. 5; S. 456
Hauptverfasser:	Funk, Juergen, Clement, Gregoire, Togninalli, Matteo, Cohen, Yaniv, Albrecht, Tom, Sullivan, Ruth, Pous, Josep Arus, Tecilla, Marco, Romero-Palomo, Fernando, Abramowski, Amadeusz, D'Annunzio, Angelo, Li, Yun Yvonna, Nguyen, Trung, Hu, Fangyao, Schumacher, Vanessa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.07.2025
Schlagworte:	Algorithms Animals Deep Learning Liver - pathology Multiple-Instance Learning Algorithms Rats Supervised Machine Learning Toxicity Tests - methods toxicology digital pathology liver multiple instance learning transformer artificial intelligence generalized lesion detection
ISSN:	1533-1601, 1533-1601
Online-Zugang:	Weitere Angaben
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