Published 2023

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Publication details

Journal : Scientific Reports , vol. 13 , p. 1–13–12 , 2023

Publisher : Springer Nature

International Standard Numbers :
Printed : 2045-2322
Electronic : 2045-2322

Publication type : Academic article

Contributors : Syed, Shaheen; Anderssen, Kathryn; Stormo, Svein Kristian; Kranz, Mathias

Issue : 1

Research areas

Quality and measurement methods

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Kjetil Aune
Chief Librarian


Fully supervised semantic segmentation models require pixel-level annotations that are costly to obtain. As a remedy, weakly supervised semantic segmentation has been proposed, where image-level labels and class activation maps (CAM) can detect discriminative regions for specific class objects. In this paper, we evaluated several CAM methods applied to different convolutional neural networks (CNN) to highlight tissue damage of cod fillets with soft boundaries in MRI. Our results show that different CAM methods produce very different CAM regions, even when applying them to the same CNN model. CAM methods that claim to highlight more of the class object do not necessarily highlight more damaged regions or originate from the same high discriminatory regions, nor do these damaged regions show high agreement across the different CAM methods. Additionally, CAM methods produce damaged regions that do not align with external reference metrics, and even show correlations contrary to what can be expected.