mlsanityData to 14 Oct 2026

Hierarchical Transformer for Electrocardiogram Diagnosis

arXiv 2411.00755 ↗First posted 1 Nov 2024cs.LGcited by 3

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Abstract (arXiv)

We propose a hierarchical Transformer for ECG analysis that combines depth-wise convolutions, multi-scale feature aggregation via a CLS token, and an attention-gated module to learn inter-lead relationships and enhance interpretability. The model is lightweight, flexible, and eliminates the need for complex attention or downsampling strategies.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

5 of 18 references.

References · 5

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  5. 2024DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attentioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025ECG-Soup: Harnessing Multi-Layer Synergy for ECG Foundation Modelscited by 1
  2. 2026How to Build Robust, Scalable Models for GSV-Based Indicators in Neighborhood Researchcited by 0
  3. 2026HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detectioncited by 0