2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model
Xingyu Li, Min Cen, Jinfeng Xu +2
Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology.Pre-trained neural networks based on ImageNet da…
A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer
Xingyu Li, Jitendra Jonnagaddala, Shuhua Yang +2
Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However,…