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Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
- Source :
- Machine.Learning.for.Biomedical.Imaging. 2 (2024)
- Publication Year :
- 2024
-
Abstract
- Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variety of CAM algorithms exist. But for segmentation tasks, only one CAM algorithm for the interpretation of the output of a CNN exist. We propose a transfer between existing classification- and segmentation-based methods for more detailed, explainable, and consistent results which show salient pixels in semantic segmentation tasks. The resulting Seg-HiRes-Grad CAM is an extension of the segmentation-based Seg-Grad CAM with the transfer to the classification-based HiRes CAM. Our method improves the previously-mentioned existing segmentation-based method by adjusting it to recently published classification-based methods. Especially for medical image segmentation, this transfer solves existing explainability disadvantages.<br />Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:023
Details
- Database :
- arXiv
- Journal :
- Machine.Learning.for.Biomedical.Imaging. 2 (2024)
- Publication Type :
- Report
- Accession number :
- edsarx.2409.20287
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.59275/j.melba.2024-ebd3