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Akademický článek

Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E‐stained images: achieving state‐of‐the‐art predictive performance with fewer data using Swin Transformer

Autor: Bangwei Guo, Xingyu Li, Miaomiao Yang, Jitendra Jonnagaddala, Hong Zhang, Xu Steven Xu

Publikováno v: The Journal of Pathology: Clinical Research, Vol 9, Iss 3, Pp 223-235 (2023)

Abstract Many artificial intelligence models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep learning networks require large training datase

Externí odkaz: https://doaj.org/article/8eafad7e89b0475797747539da604015

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Prognostic Significance of Tumor-Infiltrating Lymphocytes Determined Using LinkNet on Colorectal Cancer Pathology Images

Autor: Anran Liu, Xingyu Li, Hongyi Wu, Bangwei Guo, Jitendra Jonnagaddala, Hong Zhang, Steven Xu

Publikováno v: JCO Precision Oncology.

PURPOSE Tumor-infiltrating lymphocytes (TILs) have a significant prognostic value in cancers. However, very few automated, deep learning-based TIL scoring algorithms have been developed for colorectal cancer (CRC). MATERIALS AND METHODS We developed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::31e466d97ea83600bd3036547771ee4e
https://doi.org/10.1200/po.22.00522

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A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations

Autor: Bangwei Guo, Xingyu Li, Miaomiao Yang, Hong Zhang, Xu Steven Xu

Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainstream strategy for predicting genetic alterations using WSIs although b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04484125a3c9989fa412f0645c7bcbe6