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Arunava Chakravarty

Arunava Chakravarty
Arunava Chakravarty
 
PhD (Computer Science and Engineering), IIIT Hyderabad

Contact Details

 
079-68261542
 
# 1201, FB-1, DAU, Gandhinagar, Gujarat, India – 382007
 
https://scholar.google.co.in/citations?user=Tk2svbAAAAAJ&hl=en
https://orcid.org/0000-0003-3646-0650
https://www.scopus.com/authid/detail.uri?authorId=59102807500
https://github.com/arunava555/

Biography

Arunava Chakravarty is currently serving as an Assistant Professor at Dhirubhai Ambani University (formerly DA-IICT), Gandhinagar, India.

Prior to joining DAU, he was a Postdoctoral Researcher at the Ophthalmic Image Analysis (OPTIMA) Lab, Medical University of Vienna, Austria, under Prof. Hrovoje Bogunović. His work focused on developing deep learning frameworks for self-supervised learning over longitudinal medical data and survival analysis to model disease progression in retinal OCT, with a particular emphasis on Age-Related Macular Degeneration (AMD).

During 2019–2020, he served as a Postdoctoral Research Consultant at the Kharagpur Learning Imaging and Visualization (KLIV) Group, Indian Institute of Technology Kharagpur, India, under Prof. Debdoot Sheet, where he worked on the analysis and automated detection of abnormalities in musculoskeletal X-ray images, chest radiographs, and mammograms.

Dr. Chakravarty obtained his Ph.D. in Computer Science (2019) from the Center for Visual Information Technology, International Institute of Information Technology Hyderabad, India, where he worked on retinal image analysis for glaucoma detection from color fundus images and intra-retinal layer segmentation in OCT volumes. He received his M.Tech. (2011) from the Indian Institute of Technology (Indian School of Mines), Dhanbad, India, and his B.Tech. (2009) from the West Bengal University of Technology, India.

His research interests broadly include medical image analysis, deep learning for longitudinal and survival modeling, self-supervised representation learning, retinal imaging, and disease progression forecasting.

Specialization

Medical Image Analysis, Machine Learning for Healthcare, Computer Vision

Publications

JOURNALS

  • A. Chakravarty, T. Emre, O. Leingang, S. Riedl, J. Mai, H.P. Scholl, S. Sivaprasad, D. Rueckert, A. Lotery, U. Schmidt-Erfurth, H. Bogunovic, “Morph-SSL: Self-Supervision with Longitudinal Morphing for Forecasting AMD Progression from OCT Volumes”, IEEE Transactions on Medical Imaging, 2024, 43(9), pp.3224-3239.
  • T. Emre., A. Chakravarty, A.Rivail, D. Lachinov, O. Leingang, S. Riedl ... & H. Bogunović, “3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs”, IEEE Transactions on Medical Imaging, 2024, 43(9), pp.3200-321.
  • D. Lachinov, A. Chakravarty, C. Grechenig, U. Schmidt-Erfurth, H. Bogunovic, “Learning Spatio-temporal Model of Disease Progression with Neural-ODEs from Longitudinal Volumetric Data”, IEEE Transactions on Medical Imaging, 2023, 43(3), pp.1165-1179.
  • A. Chakravarty, J. Sivaswamy, “RACE-net: A Recurrent Neural Network for Biomedical Image Segmentation”, IEEE Journal of Biomedical and Health Informatics, 2019, 23(3), pp.1151-1162.
  • A. Chakravarty, J. Sivaswamy, “A Supervised Joint Multi-layer Segmentation Framework for Retinal Optical Coherence Tomography Images using Conditional Random Field”, Computer Methods and Programs in Biomedicine, 2018, 165, pp.235-250.
  • A. Chakravarty, J. Sivaswamy, “Joint optic disc and cup boundary extraction from monocular fundus images”, Computer Methods and Programs in Biomedicine, 2017, 147, pp.51-61.
  • L. Chakrabarty, G.D. Joshi, A. Chakravarty, G.V. Raman, S.R. Krishnadas, “Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs”, Journal of Glaucoma, 2016, 25(7), pp.590-597.
  • J. Sivaswamy, S.R. Krishnadas, A. Chakravarty, G.D. Joshi, et. al. “A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis”, JSM Journal of Biomedical Imaging Data Papers, 2015, 2(1), p.1004.

CONFERENCES

  • A. Chakravarty, T. Emre, D. Lachinov, A. Rivail, H. Scholl, L. Fritsche, S. Sivaprasad, D. Rueckert, A. Lotery, U. Schmidt-Erfurth, H. Bogunović, “Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT”, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024, pp. 273-283.
  • T. Emre, A. Chakravarty, D. Lachinov, A. Rivail, U. Schmidt-Erfurth, H. Bogunović, “Learning Temporal Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations”, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024, pp. 196-206.
  • A. Chakravarty, T. Emre, D. Lachinov, A. Rivail, U. Schmidt-Erfurth, H. Bogunovic, “Predicting the individual risk of AMD progression from retinal OCT with intra-subject temporal consistency”, Medical Imaging in Deep Learning (MIDL), 2024, pp. 273-283.
  • T. Emre, A. Chakravarty, A. Rivail, S. Riedl, U. Schmidt-Erfurth, H. Bogunović, “TINC: Temporally Informed Non-contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes”, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022, pp. 625-634.
  • A. Chakravarty, A. Kar, R. Sethuraman, D. Sheet, “Federated learning for site aware chest radiograph screening”, IEEE International Symposium on Biomedical Imaging (ISBI), 2021, pp. 1077-1081.
  • A. Chakravarty, T. Sarkar, N. Ghosh, R. Sethuraman, D. Sheet, “Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening”, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020, pp. 1234-1237.
  • A. Mitra, A. Chakravarty, N. Ghosh, T. Sarkar, R. Sethuraman, D. Sheet, “A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs”, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020, pp. 1225-1228.
  • S. Chandra, A. Chakravarty, N. Ghosh, T. Sarkar, R. Sethuraman, D. Sheet, “A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms”, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020, pp. 1128-1131.
  • A. Chakravarty, Divya Jyothi Gaddipati, J. Sivaswamy, “Construction of a Retinal Atlas for Macular OCT Volumes”, International Conference on Image Analysis and Recognition (ICIAR), 2018, pp. 650-658.
  • A. Chakravarty, J. Sivaswamy, “End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Coherence Tomography”, Annual Conference on Medical Image Understanding and Analysis (MIUA), 2017, pp. 3-14.
  • A. Chakravarty, J. Sivaswamy, “Glaucoma Classification with a Fusion of Segmentation and Image-based Features”, International Symposium on Biomedical Imaging (ISBI), 2016, pp. 689-692.
  • Ujjwal, A. Chakravarty, J. Sivaswamy, “An assistive annotation system for retinal images”, International Symposium on Biomedical Imaging (ISBI), 2015, pp. 1506-1509.
  • A. Chakravarty, J. Sivaswamy, “Coupled sparse dictionary for depth-based cup segmentation from single color fundus image”, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014, pp. 747-754.
  • M.J.J.P. van Grinsven, A. Chakravarty, J. Sivaswamy, T. Theelen, et. al., “A Bag of Words approach for discriminating between retinal images containing exudates or drusen”, International Symposium on Biomedical Imaging (ISBI), 2013, pp. 1444-1447.
  • Ujjwal, K.S. Deepak, A. Chakravarty, J. Sivaswamy, “Visual saliency based bright lesion detection and discrimination in retinal images”, International Symposium on Biomedical Imaging (ISBI), 2013, pp. 1436-1439.
  • A. Chakravarty, J. Sivaswamy, “A novel approach for quantification of retinal vessel tortuosity using quadratic polynomial decomposition”, IEEE Indian Conference on Medical Informatics and Telemedicine (ICMIT), IIT Kharagpur, 2013, pp. 7-12.

WORKSHOPS

  • A. Chakravarty, T. Emre, D. Lachinov, A. Rivail, U. Schmidt-Erfurth, H. Bogunovic, “Neural ODE-based disease forecasting from retinal imaging with temporal consistency”, Learning from Time Series for Health Workshop at ICLR 2024. (https://openreview.net/forum?id=KDInPbDITo).
  • A. Chakravarty, T. Emre, O. Leingang, S. Riedl, J. Mai, H.P. Scholl, S. Sivaprasad, L.G. Fritsche, D. Rueckert, A.J. Lotery, U. Schmidt-Erfurth, H. Bogunovic, “Self-supervised machine learning for individual prediction of conversion to neovascular AMD in PINNACLE study”, ARVO annual meeting, 2023 (https://iovs.arvojournals.org/article.aspx?articleid=2789810).
  • O. Leingang, H. Bogunovic, G. Reiter, A. Chakravarty, M. Menten, R. Holland, L.G. Fritsche, H.P. Scholl, D. Rueckert, S. Sivaprasad, A.J. Lotery, U. Schmidt-Erfurth, “Deep learning-based detection of advanced AMD on retinal OCT from the UK Biobank dataset on behalf of the PINNACLE Consortium”, ARVO annual meeting, 2023 (https://iovs.arvojournals.org/article.aspx?articleid=2790202).
  • T. Emre, M.Oghbaie, A. Chakravarty, A. Rivail, S. Riedl, Julia Mai, H.P.N Scholl, S. Sivaprasad, D. Rueckert, A. Lottery, U. Schmidt-Erfurth, H. Bogunovic, “Pretrained Deep 2.5 D Models for Efficient Predictive Modeling from Retinal OCT: A PINNACLE Study Report.”, International Workshop on Ophthalmic Medical Image Analysis at MICCAI, pp. 132-141, 2023.
  • O. Leingang, H. Bogunovic, G. Reiter, A. Chakravarty, M. Menten, R. Holland, L.G. Fritsche, H.P. Scholl, D. Rueckert, S. Sivaprasad, A.J. Lotery, U. Schmidt-Erfurth, “Deep learning-based detection of advanced AMD on retinal OCT from the UK Biobank dataset on behalf of the PINNACLE Consortium”, ARVO annual meeting, 2022 (https://iovs.arvojournals.org/article.aspx?articleid=2790202).
  • A. Chakravarty, N. Ghosh, D. Sheet, T. Sarkar, R. Sethuraman, “Radiologist Validated Systematic Search over Deep Neural Networks for Screening Musculoskeletal Radiographs”, Medical Imaging meets NeurIPS Workshop (Med-NeurIPS) at NeurIPS2019. (available in workshop website : https://sites.google.com/view/med-neurips-2019/Abstracts).
  • A. Chakravarty, J. Sivaswamy, “A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assessment in Retinal Color Fundus Images”, arXiv preprint arXiv:1808.01355, (REFUGE Challenge, MICCAI) 2018.

Teaching

  • IE406 Machine Learning (ongoing)
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