Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
<|reference_start|>Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation: 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.<|reference_end|>
@article{rheude2024leveraging,
    title={Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation},
    author={Tillmann Rheude, Andreas Wirtz, Arjan Kuijper, Stefan Wesarg},
    journal={Machine.Learning.for.Biomedical.Imaging. 2 (2024)},
    year={2024},
    doi={10.59275/j.melba.2024-ebd3},
    archivePrefix={arXiv},
    eprint={2409.20287},
    primaryClass={eess.IV cs.CV cs.LG}
}