523 citations · 577 across the 8 of their papers we have counts for
8 papers
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
Min Cen, Xingyu Li, Bangwei Guo +3
In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and reso…
Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines
Min Cen, Xingyu Li, Bangwei Guo +3
NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intel…
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…
Colorectal cancer survival prediction using deep distribution based multiple-instance learning
Xingyu Li, Jitendra Jonnagaddala, Min Cen +2
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the W…
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,…
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala +2
Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have show…
A Report on the Workshop on Biobanking Informatics
Jitendra Jonnagaddala, Damian Sue
The Workshop on Biobanking Informatics in NSW 2013 (WBIN13) was held on Friday, 10 May 2013 at The Wallace Wurth Building in the University of New South Wales. This report summaris…