Hierarchical Transformer for Electrocardiogram Diagnosis

Transformers, originally prominent in NLP and computer vision, are now being adapted for ECG signal analysis. This paper introduces a novel hierarchical transformer architecture that segments the model into multiple stages by assessing the spatial size of the embeddings, thus eliminating the need for additional downsampling strategies or complex attention designs. A classification token aggregates information across feature scales, facilitating interactions between different stages of the transformer. By utilizing depth-wise convolutions in a six-layer convolutional encoder, our approach preserves the relationships between different ECG leads. Moreover, an attention gate mechanism learns associations among the leads prior to classification. This model adapts flexibly to various embedding networks and input sizes while enhancing the interpretability of transformers in ECG signal analysis.

DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention

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.