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. 2023 Mar 22;9:16. doi: 10.1038/s41523-023-00517-2

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — a The receiver operating characteristic (ROC) curve for distinguishing between Luminal disease and Non-Luminal disease by the proposed MDL-IIA model in the test cohort (n = 672). b The classification performance of the proposed MDL-IIA model in the test cohort (n = 672). c The ROC curve for distinguishing between Luminal disease and Non-Luminal disease by the proposed MDL-IIA model and the operating points of six radiologists in the observer study cohort (n = 168). d The classification performance of the proposed MDL-IIA model and six radiologists in the observer study cohort (n = 168). The 95% confidence intervals are shown as a shaded area for the ROC curve. MDL-IIA, multi-modal deep learning with intra- and inter-modality attention modules. Multi-ResNet50, multi-modal ResNet50 model. SE Squeeze-and-Excitation, PR panel of 6 readers, AI artificial intelligence, AUC area under the ROC curve.