Data for the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics
Cite this dataset:
Gotsis, Dimitrios; Kelkar, Varun; Deshpande, Rucha; Brooks, Frank; KC, Prabhat; Myers, Kyle; Zeng, Rongping; Anastasio, Mark (2023): Data for the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2773204_V3
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Dataset Description

This repository contains the training dataset associated with the 2023 Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics (DGM-Image Challenge), hosted by the American Association of Physicists in Medicine. This dataset contains more than 100,000 8-bit images of size 512x512. These images emulate coronal slices from anthropomorphic breast phantoms adapted from the VICTRE toolchain [1], with assigned X-ray attenuation coefficients relevant for breast computed tomography. Also included are the labels indicating the breast type.

The challenge has now concluded. More information about the challenge can be found here: https://www.aapm.org/GrandChallenge/DGM-Image/.

* New in V3: we added a CSV file containing the image breast type labels and example images (PNG).
Subject Technology and Engineering
Keywords Deep generative models; breast computed tomography
License CC BY
Corresponding Creator Mark Anastasio
Downloaded 59 times
Version DOI Comment Publication Date
3 10.13012/B2IDB-2773204_V3 Added add a file containing image labels (breast_types.csv), and 4 example images (PNG) from each breast type. 2023-11-14
1 10.13012/B2IDB-2773204_V1 2023-07-17
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