Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging

Autor:	Buchnajzer, Zuzanna, Dobek, Kacper, Hapke, Stanisław, Jankowski, Daniel, Krawiec, Krzysztof
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning 68T05 I.2 I.2.6 I.2.10
Druh dokumentu:	Working Paper
Popis:	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 the observed 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. Comment: 16 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.12070 Zobrazit plný text záznamu View this record from Arxiv