Instance-level quantitative saliency in multiple sclerosis lesion segmentation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866917205256437760 |
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| author | Spagnolo, Federico Molchanova, Nataliia Cuadra, Meritxell Bach Pineda, Mario Ocampo Melie-Garcia, Lester Granziera, Cristina Andrearczyk, Vincent Depeursinge, Adrien |
| author_facet | Spagnolo, Federico Molchanova, Nataliia Cuadra, Meritxell Bach Pineda, Mario Ocampo Melie-Garcia, Lester Granziera, Cristina Andrearczyk, Vincent Depeursinge, Adrien |
| contents | Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single object among multiple instances of the same class, remains largely unexplored. Such explanations are particularly important in multi-lesional diseases to understand what drives the detection and contouring of a specific lesion. We propose instance-level explanation maps for semantic segmentation by extending SmoothGrad and Grad-CAM++ to obtain quantitative instance saliency. These methods were applied to the segmentation of white matter lesions (WMLs), a magnetic resonance imaging biomarker in multiple sclerosis. We used 4023 FLAIR and MPRAGE MRI scans from 687 patients collected at the University Hospital of Basel, Switzerland, with WML masks annotated by four expert clinicians. Three deep learning architectures, a 3D U-Net, nnU-Net, and Swin UNETR, were trained and evaluated, achieving normalized Dice scores of 0.71, 0.78, and 0.80, respectively. Instance saliency maps showed that the models relied primarily on FLAIR rather than MPRAGE for WML segmentation, with positive saliency inside lesions and negative saliency in their immediate neighborhood, consistent with clinical practice. Peak saliency values differed significantly across correct and incorrect predictions, suggesting that quantitative instance saliency may help identify segmentation errors. In conclusion, we introduce two architecture-agnostic XAI methods that provide quantitative instance-level explanations for semantic segmentation and support clinically meaningful interpretation of model decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instance-level quantitative saliency in multiple sclerosis lesion segmentation Spagnolo, Federico Molchanova, Nataliia Cuadra, Meritxell Bach Pineda, Mario Ocampo Melie-Garcia, Lester Granziera, Cristina Andrearczyk, Vincent Depeursinge, Adrien Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single object among multiple instances of the same class, remains largely unexplored. Such explanations are particularly important in multi-lesional diseases to understand what drives the detection and contouring of a specific lesion. We propose instance-level explanation maps for semantic segmentation by extending SmoothGrad and Grad-CAM++ to obtain quantitative instance saliency. These methods were applied to the segmentation of white matter lesions (WMLs), a magnetic resonance imaging biomarker in multiple sclerosis. We used 4023 FLAIR and MPRAGE MRI scans from 687 patients collected at the University Hospital of Basel, Switzerland, with WML masks annotated by four expert clinicians. Three deep learning architectures, a 3D U-Net, nnU-Net, and Swin UNETR, were trained and evaluated, achieving normalized Dice scores of 0.71, 0.78, and 0.80, respectively. Instance saliency maps showed that the models relied primarily on FLAIR rather than MPRAGE for WML segmentation, with positive saliency inside lesions and negative saliency in their immediate neighborhood, consistent with clinical practice. Peak saliency values differed significantly across correct and incorrect predictions, suggesting that quantitative instance saliency may help identify segmentation errors. In conclusion, we introduce two architecture-agnostic XAI methods that provide quantitative instance-level explanations for semantic segmentation and support clinically meaningful interpretation of model decisions. |
| title | Instance-level quantitative saliency in multiple sclerosis lesion segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.09335 |