Data for the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics

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. Please follow the instructions given on the following page in order to register for the challenge: https://www.aapm.org/GrandChallenge/DGM-Image/.

[1] Badano, Aldo, et al. "Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in-silico imaging trial." JAMA network open 1.7 (2018): e185474-e185474