Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEIT...

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Auteurs principaux: Wei-Hsiang Yu, Chih-Hao Li, Ren-Ching Wang, Chao-Yuan Yeh, Shih-Sung Chuang
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
artificial intelligence
digital pathology
quantitative morphology
primary intestinal T-cell lymphoma
convolutional neural network
human-interpretable AI
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Accès en ligne:https://doaj.org/article/d780586919ad4d8ca7742f4a7cb443e0
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https://doaj.org/article/d780586919ad4d8ca7742f4a7cb443e0

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