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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.
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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
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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.
Scopus22023 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.
Scopus6 Europe PMC42022 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.
Scopus3 Europe PMC22022 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.
Scopus3 Europe PMC32021 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.
Scopus84 Europe PMC282021 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).
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.
2017 Kurian, N. C., Patel, K., & George, N. V. (2017). Robust active noise control: An information theoretic learning approach. Applied Acoustics, 117, 180-184.
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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.
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.
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.
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.
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.
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.
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.
Scopus32022 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.
Scopus12022 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.
Scopus12022 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.
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.
Scopus132021 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.
Scopus12021 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.
Scopus92020 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.
Scopus122019 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.
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.
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Offices Held
Date Office Name Institution Country 2022 - 2023 Senior Applied Researcher Fujitsu Research India
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