ORCID as entered in ROS

Select Publications
2023, DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture,
,2023, Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines, , http://dx.doi.org/10.48550/arxiv.2302.10406
,2022, Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers, , http://dx.doi.org/10.48550/arxiv.2208.11518
,2022, Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer, , http://dx.doi.org/10.48550/arxiv.2208.10495
,2022, Colorectal cancer survival prediction using deep distribution based multiple-instance learning, , http://dx.doi.org/10.48550/arxiv.2204.11294
,2021, Characterization of long-term patient-reported symptoms of COVID-19: an analysis of social media data, , http://dx.doi.org/10.1101/2021.07.13.21260449
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