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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.12070 |
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| _version_ | 1866917323122671616 |
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| author | Buchnajzer, Zuzanna Dobek, Kacper Hapke, Stanisław Jankowski, Daniel Krawiec, Krzysztof |
| author_facet | Buchnajzer, Zuzanna Dobek, Kacper Hapke, Stanisław Jankowski, Daniel Krawiec, Krzysztof |
| contents | Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects, typically representing well-defined categories. This study proposes a class of neurosymbolic systems that learn by reconstructing images in terms of visual primitives and are thus forced to form high-level, structural explanations of them. When applied to the task of diagnosing abnormalities in histological imaging, the method proved superior to a conventional deep learning architecture in terms of classification accuracy, while being more transparent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12070 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging Buchnajzer, Zuzanna Dobek, Kacper Hapke, Stanisław Jankowski, Daniel Krawiec, Krzysztof Computer Vision and Pattern Recognition Machine Learning 68T05 I.2; I.2.6; I.2.10 Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects, typically representing well-defined categories. This study proposes a class of neurosymbolic systems that learn by reconstructing images in terms of visual primitives and are thus forced to form high-level, structural explanations of them. When applied to the task of diagnosing abnormalities in histological imaging, the method proved superior to a conventional deep learning architecture in terms of classification accuracy, while being more transparent. |
| title | Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T05 I.2; I.2.6; I.2.10 |
| url | https://arxiv.org/abs/2411.12070 |