Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning

Pranav Kulkarni, Adway Kanhere, Eliot Siegel, Paul Yi, Vishwa Sanjay Parekh
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:821-837, 2026.

Abstract

Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, resulting in increased storage and bandwidth requirements for hosting and accessing them. Since different researchers have different use cases and require different resolutions or formats for DL, it is neither feasible to anticipate every researcherś needs nor practical to store data in multiple resolutions and formats. To that end, we propose the Medical Image Streaming Toolkit (MIST), a format-agnostic database that enables streaming of medical images at different resolutions and formats from a single high-resolution copy. We evaluated MIST across eight popular, large-scale medical imaging datasets spanning different body parts, modalities, and formats. Our results showed that our framework reduced the storage and bandwidth requirements for hosting and downloading datasets without impacting image quality. We demonstrate that MIST addresses the challenges posed by large-scale medical imaging datasets by building a data-efficient and format-agnostic database to meet the diverse needs of researchers and reduce barriers to DL research in medical imaging.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-kulkarni26a, title = {Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning}, author = {Kulkarni, Pranav and Kanhere, Adway and Siegel, Eliot and Yi, Paul and Parekh, Vishwa Sanjay}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {821--837}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/kulkarni26a/kulkarni26a.pdf}, url = {https://proceedings.mlr.press/v301/kulkarni26a.html}, abstract = {Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, resulting in increased storage and bandwidth requirements for hosting and accessing them. Since different researchers have different use cases and require different resolutions or formats for DL, it is neither feasible to anticipate every researcherś needs nor practical to store data in multiple resolutions and formats. To that end, we propose the Medical Image Streaming Toolkit (MIST), a format-agnostic database that enables streaming of medical images at different resolutions and formats from a single high-resolution copy. We evaluated MIST across eight popular, large-scale medical imaging datasets spanning different body parts, modalities, and formats. Our results showed that our framework reduced the storage and bandwidth requirements for hosting and downloading datasets without impacting image quality. We demonstrate that MIST addresses the challenges posed by large-scale medical imaging datasets by building a data-efficient and format-agnostic database to meet the diverse needs of researchers and reduce barriers to DL research in medical imaging.} }
Endnote
%0 Conference Paper %T Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning %A Pranav Kulkarni %A Adway Kanhere %A Eliot Siegel %A Paul Yi %A Vishwa Sanjay Parekh %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-kulkarni26a %I PMLR %P 821--837 %U https://proceedings.mlr.press/v301/kulkarni26a.html %V 301 %X Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, resulting in increased storage and bandwidth requirements for hosting and accessing them. Since different researchers have different use cases and require different resolutions or formats for DL, it is neither feasible to anticipate every researcherś needs nor practical to store data in multiple resolutions and formats. To that end, we propose the Medical Image Streaming Toolkit (MIST), a format-agnostic database that enables streaming of medical images at different resolutions and formats from a single high-resolution copy. We evaluated MIST across eight popular, large-scale medical imaging datasets spanning different body parts, modalities, and formats. Our results showed that our framework reduced the storage and bandwidth requirements for hosting and downloading datasets without impacting image quality. We demonstrate that MIST addresses the challenges posed by large-scale medical imaging datasets by building a data-efficient and format-agnostic database to meet the diverse needs of researchers and reduce barriers to DL research in medical imaging.
APA
Kulkarni, P., Kanhere, A., Siegel, E., Yi, P. & Parekh, V.S.. (2026). Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:821-837 Available from https://proceedings.mlr.press/v301/kulkarni26a.html.

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