Nikhil Cherian Kurian

University of South Australia — Australia

Computer Science · Physical Sciences

9h-index306citations57works0.42yr avg
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Research Topics

AI in cancer detection(44), Radiomics and Machine Learning in Medical Imaging(33), Digital Imaging for Blood Diseases(10), Medical Image Segmentation Techniques(5), Cervical Cancer and HPV Research(5)

Publications57 total

A scoping review of silent trials for medical artificial intelligence
Nature Health·2026Open Access
What can reader studies of radiologist use of AI models teach us about adaptation? A signal detection modelling exploration
2026Open Access
What can reader studies of radiologist use of AI models teach us about adaptation? A signal detection modelling exploration
PsyArXiv (OSF Preprints)·2026Open Access
Development of a Deep Learning Model to Automatically Identify Palatal Landmarks on Three‐Dimensional Maxillary Dental Casts
International Journal of Dentistry·2026Open Access
A Comparative Study of Deep Neural Network Architectures in Magnification Invariant Breast Cancer Histopathology Image Analysis
Communications in computer and information science·2025
Supplementary Figure S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Pathogen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
2025· 1 cited
Supplementary Table S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Table 2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Table S2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Figure 1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Table 1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Supplementary Figure S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Data from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
Figure 2 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
2025Open Access
How is Bias Learned in Medical Image Analysis Models? An Exploration of the Encoding of Demographic Information in Deep Learning Models Trained to Detect Abnormalities on Chest X-Rays
SSRN Electronic Journal·2025Open Access
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Frequent Co-authors

Amit Sethi(41), Peter H. Gann(13), Neeraj Kumar(12), Ruchika Verma(12), Swapnil Rane(11), Stephanie M. McGregor(11), Abhijeet Patil(8), Sahar Almahfouz Nasser(6), Anita Grigoriadis(4), Pranav Jeevan(4), Gregory Verghese(3), Mengyuan Li(3), Sunil Patel(3), Aniket Bhatia(3), Sammed Mangale(3), Akhila Krishna(3), Lana Tikhomirov(3), Kashyap Patel(2), Nithin V. George(2), Subhash Yadav(2)