35 citations · 43 across the 8 of their papers we have counts for
4 papers · 1 filter
Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers
Anran Liu, Xingyu Li, Hongyi Wu +4
Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognostic values in cancers. However, very few automated, deep-learning-based TIL scoring algorithms have been devel…
Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer
Bangwei Guo, Xingyu Li, Jitendra Jonnagaddala +2
Artificial intelligence (AI) models have been developed for predicting clinically relevant biomarkers, including microsatellite instability (MSI), for colorectal cancers (CRC). How…
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations
Bangwei Guo, Xingyu Li, Miaomiao Yang +2
Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainst…
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
Zihan Chen, Xingyu Li, Miaomiao Yang +2
Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images (WSIs). It is commonly assumed that tumor regions…