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. 2022 Aug 9;14:111. doi: 10.1186/s13195-022-01043-2

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Quantitative comparisons between the multi-input VGG16 model with Grad-CAM (red) and our multi-input Conv-Att model with soft label (blue), as measured by the IoUs between the heat maps and two types of ROIs, (a) whole-drawing ROIs and (b) expert ROIs, as a function of the percentage of the number of pixels used in the heat maps. Example images with corresponding ROIs are shown at the top of each panel. Our proposed model is more similar to both whole-drawing and expert ROIs than Grad-CAM model and the higher similarity is consistent over broad range of % total number of pixels from the models’ outputs (10%-80%)