XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification
This addresses the problem of suboptimal classification performance in multi-view medical imaging due to disregarded cross-view correlations, though it appears incremental as it adapts Mamba to a specific domain.
The paper tackles multi-view medical image classification by proposing XFMamba, a Mamba-based cross-fusion architecture that introduces a two-stage fusion strategy to learn single-view features and cross-view disparities, outperforming existing convolution-based and transformer-based methods on three public datasets (MURA, CheXpert, DDSM).
Compared to single view medical image classification, using multiple views can significantly enhance predictive accuracy as it can account for the complementarity of each view while leveraging correlations between views. Existing multi-view approaches typically employ separate convolutional or transformer branches combined with simplistic feature fusion strategies. However, these approaches inadvertently disregard essential cross-view correlations, leading to suboptimal classification performance, and suffer from challenges with limited receptive field (CNNs) or quadratic computational complexity (transformers). Inspired by state space sequence models, we propose XFMamba, a pure Mamba-based cross-fusion architecture to address the challenge of multi-view medical image classification. XFMamba introduces a novel two-stage fusion strategy, facilitating the learning of single-view features and their cross-view disparity. This mechanism captures spatially long-range dependencies in each view while enhancing seamless information transfer between views. Results on three public datasets, MURA, CheXpert and DDSM, illustrate the effectiveness of our approach across diverse multi-view medical image classification tasks, showing that it outperforms existing convolution-based and transformer-based multi-view methods. Code is available at https://github.com/XZheng0427/XFMamba.