Integrated and Interoperable Platform for Detecting Masses on Mammograms

Chatzopoulos, Edouard;Jodogne, Sébastien
(2024) 34th Medical Informatics Europe Conference (MIE 2024) — Location: Athens, Greece (25.August.2024)

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Abstract
The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.
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Chatzopoulos, E., & Jodogne, S. (2024). Integrated and Interoperable Platform for Detecting Masses on Mammograms. Studies in Health Technology and Informatics, 316, 1103-1107. https://doi.org/10.3233/SHTI240603 (Original work published 2024)