WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912416346931200 |
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| author | Sharma, Abhinav Liu, Bojing Rantalainen, Mattias |
| author_facet | Sharma, Abhinav Liu, Bojing Rantalainen, Mattias |
| contents | Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology Sharma, Abhinav Liu, Bojing Rantalainen, Mattias Image and Video Processing Computer Vision and Pattern Recognition Methodology Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications. |
| title | WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Methodology |
| url | https://arxiv.org/abs/2403.15238 |