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

ArXi:2411.12070v5 Announce Type: replace-cross 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.