3 citations · 6 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…
DePS: An improved deep learning model for de novo peptide sequencing
Cheng Ge, Yi Lu, Jia Qu +5
De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptid…
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…