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. 2021 Apr 22;21(Suppl 1):134. doi: 10.1186/s12911-020-01340-6

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© The Author(s) 2021

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. 7 — Comparative analysis using different dimension raising methods: Copying features, Interactive and Polynomial features (IP), AutoEncoder features (AE), Denoising AutoEncoder features (DAE), and Denoising AutoEncoder with L1 regularization features (DAE-L1). Except for the different dimension raising methods, the other experimental settings were controlled as follows: Structured data (580D) + Pathological image (3, 4, 5 convolution layer, 1280D) + Fully connected layers (500, 100, 2)