Adway KanhereMedical Image Analysis | Artificial Intelligence
Education
The Johns Hopkins University, USA, Doctor of Philosophy, Computer Science
2025 -

Machine Learning, Artifical Intelligence, Natural Language Processing

The Johns Hopkins University, USA, Master of Science & Engineering Degree, Biomedical Engineering
2021- 2023

Specialization in Biomedical Data Science with Thesis

M.S Ramaiah Institute of Technology, India, Bachelor of Engineering Degree, Biomedical Engineering
2016 - 2020
Work Experience
University of Maryland School of Medicine, USA & University of Maryland Institute of Health Computing, USA (Jul 2024-), Bioinformatics Software Engineer-II
Jun 2023 – Aug 2025 | Full-Time
  • •Purpose: Lead engineering projects to build automated AI solutions that enhance clinical workflows and improve patient care.

  • •Tech Stack: PyTorch, FastChat, Docker, Apache Airflow, Kubernetes

  • •Responsibilities and Outcomes:
  • •Setup and deployed a medical imaging provisioning toolkit based on the Kaapana backend using a custom container registry.
  • •Investigated the use of LLaVa-Med and GPT4-Vision for visual question answering of diagnostic conditions using radiological images.
  • •Developed and deployed natural language processing pipelines leveraging Vicuna 7B and 13B models backed by very large language models (VLLMs) to extract Agatston coronary artery calcium scores from radiology reports using prompt engineering.
  • •Configured and deployed an online DICOM Viewer “Vision” for visualizing 3D DICOM scans based on OHIF, setup connectivity to custom DICOMWeb server backend. Added automated reporting, segmentation, and active learning capabilities using MONAILabel.
  • •Contributed to developing an existing codebase to pull imaging data from PACS/VNA, automate anonymization of image data in accordance to HIPAA.
  • •Developed “Maverick”, an AutoML tool for medical image analysis and segmentation - integrated with Weights&Biases and built using Streamlit.
    • Regeneron Pharmaceuticals, USA, Data Science Intern - Early Clinical Development
    Jun 2022 – Aug 2022 | Internship
  • •Purpose: Apply data science and deep learning to advance oncology research and improve drug response.

  • •Tech Stack: Python, PyTorch, nnUNet, Streamlit

  • •Responsibilities and Outcomes:
  • •Developed a deep learning based pipeline for automated segmentation of lung tumor volumes from lung CT images on the NSCLC-Radiomics (TCIA) dataset.
  • •Developed a CNN based regression pipeline for progression-free survival, overall survival prediction and response to immunotherapy for patients with non-small cell lung cancer.
  • •Designed and deployed an internal web application based GUI to translate the trained AI model into a tool that can be used interactively by scientists.
    • Johns Hopkins University & School of Medicine, Graduate Research Assistant
    Dec 2021 – May 2022 | Part-Time
  • •Purpose: Conduct deep learning research leveraging generative AI to advance medical imaging capabilities with smartphone devices.

  • •Tech Stack: Python, PyTorch, Detectron2, TorchXRayVision

  • •Responsibilities and Outcomes:
  • •Programmed a deep learning-based pipeline for transforming smartphone-based photos of chest X-Ray images to digital X-Ray images using a Pix2Pix Generative Adversarial Network (GAN).
  • •Programmed a deep learning-based pipeline for localizing the region of the thorax in chest X-Ray images using Detectron2's pipeline.
  • •Analyzed the performance of existing state-of-art CNN-based algorithms in TorchXRayVision to generalize the prediction of lung abnormalities on smartphone photographs of chest X-rays compared to synthetically modified digital chest X-Ray images.
    • Peer-reviewed publications
    * First/Co-first author

    Kanhere, A.*, Kulkarni, P., Yi, P., & Parekh, V. (2024). Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2380–2387). (Best Poster Award)

    Kanhere, A.*, Navarathna, N.*, Yi, P., Parekh, V., Pickle, J., Luber, J., Cloak, C., Ernst, T., Chang, L., Li, D., Redline, S., & Isaiah, A. (2024). Unlocking Hidden Data: Deep Learning for Opportunistic Airway Analysis in Large-Scale Pediatric Neuroimaging. American Journal of Respiratory and Critical Care Medicine.

    Chatterjee, D.*, Kanhere, A.*, Doo, F., Zhao, J., Chan, A., Welsh, A., Kulkarni, P., Trang, A., Parekh, V., & Yi, P. (2024). Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population. Journal of Imaging Informatics in Medicine, 1–14.

    Kamel, P.*, Khalid, M., Steger, R., Kanhere, A., Kulkarni, P., Parekh, V., Yi, P., Gandhi, D., & Bodanapally, U. (2024). Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts. Journal of Imaging Informatics in Medicine, 1–12.

    Doo, F.*, Savani, D.*, Kanhere, A., Carlos, R., Joshi, A., Yi, P., & Parekh, V. (2024). Optimal Large Language Model Characteristics to Balance Accuracy and Energy Use for Sustainable Medical Applications. Radiology, 312(2), e240320.

    Kargilis, D.*, Kulkarni, P., Kanhere, A., Garin, S., Murphy, Z., Hafey, C., Parekh, V. S., & Yi, P. H. (2024). The Impact of Standard Image Preprocessing on Deep Learning Models’ Predictions for Chest Radiographs: An Overlooked Source of Performance Variability. Journal of Imaging Informatics in Medicine (Under Review).

    Kamel, P.*, Kanhere, A., Kulkarni, P., Khalid, M., Steger, R., Bodanapally, U., Gandhi, D., Parekh, V., & Yi, P. (2024). Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences. Journal of Imaging Informatics in Medicine, 1–10.

    Kulkarni, P.*, Kanhere, A., Siegel, E., Yi, P., & Parekh, V. (2024). ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging. Journal of Imaging Informatics in Medicine, 1–14.

    Kulkarni, P.*, Kanhere, A., Yi, P. H., & Parekh, V. S. From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification. Proceedings of the 4th Machine Learning for Health Symposium (ML4H), 2024.

    Kulkarni, P.*, Kanhere, A., Kukreja, H., Zhang, V., Yi, P. H., & Parekh, V. S.# (2024). Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning. MIDL 2024 (Medical Imaging with Deep Learning): Short Paper Track.

    Kulkarni, P.*, Kanhere, A.*, Savani, D.*, Chan, A., Chatterjee, D., Yi, P. H., & Parekh, V. S.# (2024). Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations. MIDL 2024 (Medical Imaging with Deep Learning): Short Paper Track

    Yi, P. H.*, Bachina, P., Bharti, B., Garin, S. P., Kanhere, A., Kulkarni, P., Li, D., Parekh, V. S., Santomartino, S. M., Moy, L., & Sulam, J. (2024). Pitfalls and Best Practices in Evaluation of Algorithmic Biases in Radiology. Radiology (In Press).

    Doo, F.*, Parekh, V., Kanhere, A., Savani, D., Tejani, A., Sapkota, A., & Paul, H. (2024). Evaluation of climate-aware metrics tools for radiology informatics and artificial intelligence: toward a potential radiology ecolabel. Journal of the American College of Radiology, 21(2), 239–247.

    Bachina, P.*, Garin, S., Kulkarni, P., Kanhere, A., Sulam, J., Parekh, V., & Yi, P. (2023). Coarse race and ethnicity labels mask granular underdiagnosis disparities in deep learning models for chest radiograph diagnosis. Radiology, 309(2), e231693.

    Kulkarni, P.*, Kanhere, A., Yi, P. H., & Parekh, V. S. (2022). From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning. Medical Imaging meets NeurIPS Workshop, 2022 Conference on Neural Information Processing Systems

    PrePrints

    Kulkarni, P.*, Kanhere, A., Siegel, E., Yi, P. H., & Parekh, V. S. (2023). One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale. arXiv preprint arXiv:2307.00438.

    Kulkarni, P.*, Kanhere, A., Yi, P. H., & Parekh, V. S. Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery. arXiv preprint arXiv:2305.07637.

    CONFERENCE PRESENTATIONS

    * Presenting author

    Kanhere, A.*, Kulkarni, P., Yi, P., & Parekh, V. (2024). Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels. Data Curation and Augmentation in Medical Imaging Workshop at CVPR 2024. (Best Poster Award)

    Kamel, P.*, Khalid, M., Steger, R., Kanhere, A., Kulkarni, P., Parekh, V. S., Yi, P. H, Bodanapally, U., & Gandhi, D. (2024). Is Dual-Energy CT Better for Deep Learning-Based Detection and Segmentation of Early Ischemic Infarcts on CT? 2024 American Society of Neuroradiology Annual Meeting.

    Kamel, P.*, Kanhere, A., Kulkarni, P., Khalid, M., Steger, R., Bodanapally, U., Gandhi, D., Parekh, V. S., & Yi, P. H. (2024). Assessing the Generalizability of Acute Stroke Segmentation using a Self-Configuring Neural Network Trained on Public Data. 2024 American Society of Neuroradiology Annual Meeting.

    Kamel, P.*, Khalid, M., Steger, R., Kanhere, A., Kulkarni, P., Parekh, V. S., Yi, P. H., Bodanapally, U., & Gandhi, D. (2024). Cross-Modality Stroke Segmentation using Deep Convolutional Neural Networks for Detection of Acute Ischemic Infarcts on Non-Contrast Head CT. 2024 American Society of Neuroradiology Annual Meeting.

    Navarathna, N.*, Chatterjee, D., Chan, A., Kulkarni, P., Kanhere, A., Parekh, V. S., & Yi, P. H. (2024). From Download to ML: Challenges in Directly Using the MIDRC Dataset for Machine Learning and Enhancing its Usability. Spotlight Talk, 2024 Society for Imaging Informatics in Medicine Annual Meeting.

    Chatterjee, D., Kanhere, A., Trang, A., Parekh, V., Yi, P. (2023). Augmenting the MIDRC Dataset using Deep Learning-Based Quantification of Abdominal Aortic Calcification: Proof-of-Concept for Population-Level Disease Screening, Podium presentation, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine, Baltimore, MD

    Chatterjee, D., Kanhere, A., Shoum, B L., Singh, S, Parekh, V.S, Yi, P.H., Arbab-Zadeh, A. (2023). Automated Detection of Pericoronary Adipose Tissue Attenuation to Detect Inflammation on Coronary Computed Tomography Angiography, Podium presentation, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine, Baltimore, MD

    Zhao, J.*, Kanhere, A., Kulkarni, P., Chatterjee, D., Parekh, V. S., & Yi, P. H. (2024). Using Deep Learning to Predict Knee Osteoarthritis. Poster Presentation, Undergraduate Research Day 2024, University of Maryland.

    Kim, J.*, Kulkarni, P., Welsh, A., Garin, S., Chatterjee, D., Kanhere, A., Parekh, V. S, & Yi, P. H. (2023). Sex Bias in Pediatric Deep Learning Chest Radiograph Classifier Model. Spotlight Talk, Medical Student Research Day 2023, University of Maryland-Baltimore.

    Kamel, P.*, Kanhere, A., Kulkarni, P., Khalid, M., Steger, R., Bodanapally, U., Gandhi, D., Parekh, V. S., & Yi, P. H. (2023). Quantifying the Technical Challenges and DICOM Metadata Variability in Stroke Machine Learning Data Curation. Spotlight Talk, Radiological Society of North America 109th Scientific Assembly and Annual Meeting.

    Kulkarni, P.*, Kanhere, A., Yi, P. H., & Parekh, V. S. (2023). From Isolation to Collaboration: Harmonizing Heterogeneous Medical Imaging Datasets with Partial Annotations. Spotlight Talk, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine.

    Bachina, P.*, Garin, S., Kulkarni, P., Kanhere, A., Sulam, J., Parekh, V. S., & Yi, P. H. (2023). Coarse Race and Ethnicity Labels Mask Granular Underdiagnosis Disparities in Deep Learning Models for Chest Radiograph Diagnosis. Spotlight Talk, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine

    Kulkarni, P.*, Kanhere, A., Siegel, E., Yi, P. H., & Parekh, V. S. (2023). One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale. Poster Presentation, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine.

    Kulkarni, P.*, Kanhere, A., Yi, P. H., & Parekh, V. S. (2023). Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural Language Cohort Discovery. Poster Presentation, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine.

    Kanhere, A.*, Kulkarni, P., Yi, P. H., & Parekh, V. S. (2023). SegViz: A Federated Learning Framework to Train Multi-task Segmentation Models from Partially Annotated and Distributed Datasets. Poster Presentation, 2023 Conference on Machine Intelligence in Medical Imaging, Society for Imaging Informatics in Medicine.

    Bachina, P.*, Garin, S., Kulkarni, P., Kanhere, A., Kargilis, D., Parekh, V. S., & Yi, P. H. (2023). Not So Black and White: Confounders Mediate AI Prediction of Race on Chest X-Rays. Poster Presentation, Machine Learning for Healthcare 2023.

    Kamel, P.*, Kanhere, A., Kulkarni, P., Parekh, V. S., & Yi, P. H. (2023). Optimizing Acute Stroke Segmentation: Do Additional Sequences Matter for Deep Learning Algorithms? Poster Presentation, 2023 Society for Imaging Informatics in Medicine Annual Meeting.

    Patents

    Parekh, V. S., Kulkarni, P., Kanhere, A., Yi, P. H., & Siegel, E. Systems and Methods for High-Throughput Analysis for Graphical Data. US Patent Application No. 63/501,552 – Filed May 11, 2023. PCT International Patent Application No. PCT/WO2024/233969 - Filed November 14, 2024.

    Book Chapters

    Parekh, V. S., Kulkarni, P., Kanhere, A., & Jacobs, M. A. (2024). Expanding the Federated Horizon: Cross-Domain Techniques for Collective Intelligence. Federated Learning for Medical Imaging: Principles, Algorithms and Applications, The MICCAI Society Book Series (In Press).

    Awards
    Best Poster Award, Data Curation and Augmentation in Medical Imaging (DCAMI) Workshop at CVPR 2024
    Ranked 1st ($5000 Prize) HOPSTART 2022, Johns Hopkins New Venture Challenge
    Ranked 1st ($3000 Prize) FastForwardU Spark Incubator Showcase 2022, Johns Hopkins Technology Ventures (JHTV)
    National Talent Search Exam, State of Karnataka, India 2014

    State Rank #13 from 80,000 participants