3 citations · 3 across the 5 of their papers we have counts for
7 papers
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,…
CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis
Yixin Hu, Xingyu Li
Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them…
Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images
Xingyu Li
Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source sepa…
Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
Hanwen Liang, Konstantinos N. Plataniotis, Xingyu Li
Computational histopathology image diagnosis becomes increasingly popular and important, where images are segmented or classified for disease diagnosis by computers. While patholog…
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?
Xingyu Li, Konstantinos N. Plataniotis
Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse…