Nikhil Kurian

Mr Nikhil Kurian

Grant-Funded Researcher (B)

School of Public Health

College of Health

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

Dr. Nikhil Kurian is a postdoctoral researcher working on developing safe, fair, and trustworthy AI for medical image analysis. Previously, he was a senior AI researcher at Fujitsu Research of India Pvt Ltd (FRIPL). Dr Kurian completed his PhD from the Indian Institute of Technology, Bombay, in May 2023. His doctoral research focused on pioneering deep learning algorithms that automatically analyze cancerous tissue in biopsy and radiology images.

Date Position Institution name
2022 - 2023 Senior Researcher Fujitsu Research India

Date Institution name Country Title
Indian Institute of Technology Bombay India PhD
Indian Institute of Technology Gandhinagar India Master of technology

Year Citation
2025 Kurian, N. C., Gann, P. H., Kumar, N., McGregor, S. M., Verma, R., & Sethi, A. (2025). Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole Slide Images.. Cancer research communications, 5(1), 157-166.
DOI Scopus2 WoS1
2023 Patil, A., Diwakar, H., Sawant, J., Kurian, N. C., Yadav, S., Rane, S., . . . Sethi, A. (2023). Efficient quality control of whole slide pathology images with human-in-the-loop training. Journal of Pathology Informatics, 14, 100306.
DOI Scopus19
2023 Verghese, G., Li, M., Liu, F., Lohan, A., Kurian, N. C., Meena, S., . . . Grigoriadis, A. (2023). Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies. The Journal of Pathology, 260(4), 376-389.
DOI Scopus19 Europe PMC14
2022 Kanse, A. S., Kurian, N. C., Aswani, H. P., Khan, Z., Gann, P. H., Rane, S., & Sethi, A. (2022). Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases. JCO Clinical Cancer Informatics, 6(6), e2200067.
DOI Scopus5 Europe PMC3
2022 Verma, R., Kumar, N., Patil, A., Kurian, N. C., Rane, S., & Sethi, A. (2022). Author's Reply to 'MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge'. IEEE Transactions on Medical Imaging, 41(4), 1000-1003.
DOI Scopus3 Europe PMC7
2021 Verma, R., Kumar, N., Patil, A., Kurian, N. C., Rane, S., Graham, S., . . . Sethi, A. (2021). MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. IEEE Transactions on Medical Imaging, 40(12), 3413-3423.
DOI Scopus179 Europe PMC81
2021 Cherian Kurian, N., Sethi, A., Reddy Konduru, A., Mahajan, A., & Rane, S. U. (2021). A 2021 update on cancer image analytics with deep learning. WIREs Data Mining and Knowledge Discovery, 11(4).
DOI
2020 Anand, D., Kurian, N. C., Dhage, S., Kumar, N., Rane, S., Gann, P. H., & Sethi, A. (2020). Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images. Journal of Pathology Informatics, 11(1), 19.
DOI
2017 Kurian, N. C., Patel, K., & George, N. V. (2017). Robust active noise control: An information theoretic learning approach. Applied Acoustics, 117, 180-184.
DOI Scopus54

Year Citation
2026 Jeevan, P., Kurian, N. C., & Sethi, A. (2026). A Comparative Study of Deep Neural Network Architectures in Magnification Invariant Breast Cancer Histopathology Image Analysis. In Communications in Computer and Information Science Vol. 2546 CCIS (pp. 121-133). Springer Nature Switzerland.
DOI
2025 Krishna, A., Kurian, N. C., Patil, A., Parulekar, A., Pranav Jeevan, P., & Sethi, A. (2025). Pathogen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images. In Proceedings International Symposium on Biomedical Imaging (pp. 1-4). Houston, Texas, USA: IEEE.
DOI
2024 Krishna, A., Gupta, R. K., Kurian, N. C., Jeevan, P., & Sethi, A. (2024). Heterogeneous Graphs Model Spatial Relationship Between Biological Entities for Breast Cancer Diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14373 LNCS (pp. 97-106). ONline: Springer Nature Switzerland.
DOI Scopus3
2024 Kurian, N. C., & Kumar, N. (2024). Improved Multi-Step, Multi-Variate, and Spatiotemporal 5G Data Usage Forecasting Without Deploying Data Imputation Techniques. In Proceedings - IEEE Global Communications Conference, GLOBECOM (pp. 7417-7422). Online: IEEE.
DOI
2024 Kurian, N. C., Varsha, S., Patil, A., Khade, S., & Sethi, A. (2024). Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring. In Proceedings - 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering, BIBE 2023 (pp. 121-128). Online: IEEE.
DOI Scopus1
2023 Almahfouz Nasser, S., Kurian, N. C., Meena, M., Shamsi, S., & Sethi, A. (2023). WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14092 LNCS (pp. 15-24). Online: Springer Nature Switzerland.
DOI
2023 Chandra, T., Nasser, S., Kurian, N., & Sethi, A. (2023). Improving Mitosis Detection via UNet-Based Adversarial Domain Homogenizer. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (pp. 52-56). Online: SCITEPRESS - Science and Technology Publications.
DOI
2023 Gupta, R., Nandgaonkar, S., Kurian, N., Bameta, T., Yadav, S., Kaushal, R., . . . Sethi, A. (2023). EGFR Mutation Prediction of Lung Biopsy Images Using Deep Learning. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (pp. 102-109). Online: SCITEPRESS - Science and Technology Publications.
DOI
2022 Kurian, N. C., Lehan, A., Verghese, G., Dharamshi, N., Meena, S., Li, M., . . . Sethi, A. (2022). Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation. In 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 91-96). Taichung, Taiwan: IEEE.
DOI Scopus4
2022 Kurian, N. C., Varsha, S., Bajpai, A., Patel, S., & Sethi, A. (2022). Improved Histology Image Classification under Label Noise Via Feature Aggregating Memory Banks. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Vol. 2022-March (pp. 1-5). Kolkata, India: IEEE.
DOI Scopus3
2022 Varsha, S., Nasser, S. A., Bala, G., Kurian, N. C., & Sethi, A. (2022). Multi-Modal Information Fusion for Classification of Kidney Abnormalities. In ISBIC 2022 - International Symposium on Biomedical Imaging Challenges, Proceedings Vol. 103 (pp. e210000). Kolkata, India: IEEE.
DOI Scopus4 Europe PMC2
2022 Almahfouz Nasser, S., Kurian, N. C., & Sethi, A. (2022). Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis Vol. 13166 LNCS (pp. 77-80). Strasbourg, France: Springer International Publishing.
DOI
2021 Kurian, N. C., Meshram, P. S., Patil, A., Patel, S., & Sethi, A. (2021). Sample Specific Generalized Cross Entropy for Robust Histology Image Classification. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) Vol. 2021-April (pp. 1934-1938). Nice, France: IEEE.
DOI Scopus17
2021 Kurian, N. C., Singh, G., Hebbar, P., Kodate, S., Rane, S., & Sethi, A. (2021). Robust Classification of Histology Images Exploiting Adversarial Auto Encoders. In Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021-January (pp. 2871-2874). Mexico: IEEE.
DOI Scopus1
2021 Wilson, B., Kurian, N. C., Singh, A., & Sethi, A. (2021). Satellite-Derived Bathymetry Using Deep Convolutional Neural Network. In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 2280-2283). Waikoloa, HI, USA: IEEE.
DOI Scopus18
2021 Patil, A., Talha, M., Bhatia, A., Kurian, N. C., Mangale, S., Patel, S., & Sethi, A. (2021). Fast, Self Supervised, Fully Convolutional Color Normalization Of H&E Stained Images. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) Vol. 2021-April (pp. 1563-1567). Nice, France: IEEE.
DOI Scopus18
2019 Patrawala, V., Kurian, N. C., & Sethi, A. (2019). Improving Histopathology Classification using Learnable Preprocessing. In IEEE Region 10 Annual International Conference Proceedings TENCON Vol. 2019-October (pp. 2460-2465). IEEE.
DOI
2017 Patel, K., Kurian, N. C., & George, N. V. (2017). Time frequency analysis: A sparse S transform approach. In 2016 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2016 (pp. 1-4). IEEE.
DOI Scopus6

Date Office Name Institution Country
2022 - 2023 Senior Applied Researcher Fujitsu Research India

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