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Bio

Bio

Imon Banerjee is currently working as a Research Scientist at the Biomedical Data Science Dept. Starting from 2016, she was a Post-doctoral scholar in the Laboratory of Quantitative Imaging at Stanford university. She received her Ph.D. from The University of Genova, Italy in 2016. During her Ph.D., she received Marie Curie European fellowship and worked as an early-stage researcher at The Institute for Applied Mathematics and Information Technologies, National Research Council, Italy. During her Ph.D., she developed novel techniques for building patient-specific 3D computational models. She completed her Master thesis in The European Organization for Nuclear Research (CERN), Geneva. Her research is focused on developing unstructured data analysis and big data mining techniques to support clinical diagnosis and treatment.

Supervisors

Education & Certifications

  • Postdoc, Stanford, Bioinformatics (2017)
  • Ph.D., University of Genova, Computer Science (2016)
  • Master of Technology, National Institute of Technology, Durgapur, Information Technology (2011)

Contact

Publications

All Publications

  • Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Computerized medical imaging and graphics Banerjee, I., Crawley, A., Bhethanabotla, M., Daldrup-Link, H. E., Rubin, D. L. 2017

    Abstract

    This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a comprehensive representation of tumor by performing a fusion of diffusion-weighted MR scans (DWI) and gadolinium chelate-enhanced T1-weighted MR scans (MRI). Finally, we adapted transfer learning approach where a pre-trained deep convolutional neural network has been fine-tuned based on the fused images for performing classification of the two RMS subtypes. We achieved 85% cross validation prediction accuracy from the fine-tuned deep CNN model. Our system can be exploited to provide a fast, efficient and reproducible diagnosis of RMS subtypes with less human interaction. The framework offers an efficient integration between advanced image processing methods and cutting-edge deep learning techniques which can be extended to deal with other clinical domains that involve multimodal imaging for disease diagnosis.

    View details for DOI 10.1016/j.compmedimag.2017.05.002

    View details for PubMedID 28515009

  • Combination of visual and symbolic knowledge: A survey in anatomy. Computers in biology and medicine Banerjee, I., Patané, G., Spagnuolo, M. 2017; 80: 148-157

    Abstract

    In medicine, anatomy is considered as the most discussed field and results in a huge amount of knowledge, which is heterogeneous and covers aspects that are mostly independent in nature. Visual and symbolic modalities are mainly adopted for exemplifying knowledge about human anatomy and are crucial for the evolution of computational anatomy. In particular, a tight integration of visual and symbolic modalities is beneficial to support knowledge-driven methods for biomedical investigation. In this paper, we review previous work on the presentation and sharing of anatomical knowledge, and the development of advanced methods for computational anatomy, also focusing on the key research challenges for harmonizing symbolic knowledge and spatial 3D data.

    View details for DOI 10.1016/j.compbiomed.2016.11.018

    View details for PubMedID 27940289

  • Computerized Prediction of Radiological Observations Based on Quantitative Feature Analysis: Initial Experience in Liver Lesions Journal of Digital Imaging Banerjee, I. 2017

    View details for DOI 10.1007/s10278-017-9987-0

  • Radiology Report Annotation using Intelligent Word Embeddings: Applied to Multi-institutional Chest CT Cohort Journal of Biomedical Informatics Banerjee, I., et al 2017

    View details for DOI 10.1016/j.jbi.2017.11.01

  • Semantics-driven annotation of patient-specific 3D data: a step to assist diagnosis and treatment of rheumatoid arthritis VISUAL COMPUTER Banerjee, I., Agibetov, A., Catalano, C. E., Patane, G., Spagnuolo, M. 2016; 32 (10): 1337-1349

    View details for DOI 10.1007/s00371-016-1226-z

    View details for Web of Science ID 000386396800011

  • Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment NIPS 2016 Workshop on Machine Learning for Health (NIPS ML4HC) Banerjee, I. 2016
  • Generation of 3D Canonical Anatomical Models: An Experience on Carpal Bones NEW TRENDS IN IMAGE ANALYSIS AND PROCESSING - ICIAP 2015 WORKSHOPS Banerjee, I., Laga, H., Patane, G., Kurtek, S., Srivastava, A., Spagnuolo, M. 2015; 9281: 167-174

    View details for DOI 10.1007/978-3-319-23222-5_21

    View details for Web of Science ID 000364992300021

  • Semantic annotation of 3D anatomical models to support diagnosis and follow-up analysis of musculoskeletal pathologies International Journal of Computer Assisted Radiology and Surgery Banerjee, I. 2015

    View details for DOI 10.1007/s11548-015-1327-6

  • Accessing and Representing Knowledge in the Medical Field: Visual and Lexical Modalities. 3D Multiscale Physiological Human. Banerjee, I. Springer, London. 2013

    View details for DOI 10.1007/978-1-4471-6275-9_13