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. 2024 Mar 27;25:134. doi: 10.1186/s12859-024-05758-x

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© The Author(s) 2024

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 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. 5 — Real-time predictions and saliency maps from whole-slide images. Slideflow Studio provides an interface for generating both whole-slide and focal tile predictions. Right clicking on an area of the screen extracts a tile at the given location, showing the tile before and after stain normalization (if applicable). Gradient-based pixel attribution methods can be used to render saliency maps for the given image, which can be shown as a heatmap (shown here) or as an overlay (not shown)