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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. 4 — Schematic overview of the richer multilevel feature representation. We use average pooling on the final output of the third, fourth and fifth convolution layers of VGG16 network. Then, we concatenate the three vectors into a 1280D dimensional vector, which is used as the richer multilevel feature representation of the pathological image. The convolutional layer parameters are denoted as “Conv<number of convolutional layers>-<number of channels>”