Nikhil Kurian

Dr Nikhil Kurian

Post Doctoral Researcher

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

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.

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

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

  • Journals
    Year Citation
    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 Scopus2
    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 Scopus6 Europe PMC4
    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 Scopus3 Europe PMC2
    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 PMC3
    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 Scopus84 Europe PMC28
    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 Scopus42

  • Conference Papers
    Year Citation
    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
    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
    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. 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. 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 Scopus3
    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. Kolkata, India: IEEE.
    DOI Scopus1
    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. Kolkata, India: IEEE.
    DOI Scopus1
    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 Patil, A., Talha, M., Bhatia, A., Kurian, N. C., Mangale, S., Patel, S., & Sethi, A. (2021). Fast, self supervised, fully convolutional color normalization of HE stained images. In Proceedings - International Symposium on Biomedical Imaging Vol. 2021-April (pp. 1563-1567). IEEE.
    DOI Scopus13
    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 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2021 (pp. 2871-2874). United States: IEEE.
    DOI Scopus1
    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 Proceedings - International Symposium on Biomedical Imaging Vol. 2021-April (pp. 1934-1938). IEEE.
    DOI Scopus9
    2020 Wilson, B., Kurian, N. C., Singh, A., & Sethi, A. (2020). Satellite-Derived Bathymetry Using Deep Convolutional Neural Network. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2280-2283). IEEE.
    DOI Scopus12
    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. IEEE.
    DOI Scopus5

  • Offices Held
    Date Office Name Institution Country
    2022 - 2023 Senior Applied Researcher Fujitsu Research India
  • Position: Post Doctoral Researcher
  • Phone: 83133733
  • Email: nikhil.kurian@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 2
  • Org Unit: Australian Institute for Machine Learning - Projects
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