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. 2020 Feb 25;10:3398. doi: 10.1038/s41598-020-60311-z

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© The Author(s) 2020

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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Limitations of heatmaps as tool to explain black box predictions. (a) Contrast-enhanced CT images for two inguinal lymph nodes that were used as input to generate all heatmaps displayed, with (1) a lymph node positive for infiltration by PCa, and (2) a lymph node negative for infiltration. In (b,c) heatmaps produced by two CNNs trained with location balanced training data, or masked input data, respectively. Beyond verifying that the lymph node is important for classification, heatmaps provide little additional information as to why classification output was either true positive (b1,c1), true negative (b2), or false positive (c2).