Deep neural networks with Fisher vector encoding for medical image classification

ArXi:2605.01667v1 Announce Type: new Orderless encoding methods have shown to improve Convolutional Neural Networks (CNNs) for image classification in the context of limited availability of data. Additionally, hybrid CNN + Vision Transformers (ViT) models have been recently proposed to address CNN locality bias issues. These models outperformed CNN-only approaches. Despite that, the integration of such hybrid models with elaborated feature representation can be highly beneficial and remains large unexplored in the literature. In this context, we propose the