DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention

TL;DR

DuoFormer is a hierarchical transformer model that combines CNNs and Vision Transformers with a novel scale attention mechanism, improving medical image analysis especially on small datasets by capturing multi-scale features.

Contribution

It introduces a hierarchical transformer architecture with scale attention, integrating CNN features with ViT for better spatial understanding and generalization.

Findings

Outperforms baseline models on small and medium-sized medical datasets

Demonstrates efficiency and generalizability across applications

Plug-and-play design for various CNN architectures

Abstract

We here propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack of inductive biases and dependence on extensive training datasets in ViTs, our model employs a CNN backbone to generate hierarchical visual representations. These representations are then adapted for transformer input through an innovative patch tokenization. We also introduce a 'scale attention' mechanism that captures cross-scale dependencies, complementing patch attention to enhance spatial understanding and preserve global perception. Our approach significantly outperforms baseline models on small and medium-sized medical datasets, demonstrating its efficiency and generalizability. The components are designed as plug-and-play for different CNN architectures and can be adapted for multiple applications. The code is available at https://github.com/xiaoyatang/DuoFormer.git.