mlsanityData to 7 Oct 2026

POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound

arXiv 2105.09913 ↗First posted 20 May 2021eess.IV cs.CVcited by 10

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Abstract (arXiv)

The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environments and third world countries. Our contributions towards rapid large-scale testing include a novel deep learning architecture capable of analyzing ultrasound data that can run in real-time and significantly improve the current state-of-the-art detection accuracies using image-based COVID-19 detection.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 20 May 2021.

Builds on

8 of 18 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  6. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  7. 2020POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)cited by 25
  8. 2020Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysiscited by 5

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Cited by year

Citing papers per year: 2022: 6; 2023: 2; 2024: 1; 2025: 1.

10 papers.

  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  3. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  4. 2022Transformer Meets Boundary Value Inverse Problemscited by 22
  5. 2023Large-scale Long-tailed Disease Diagnosis on Radiology Imagescited by 17
  6. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
  7. 2022Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesiscited by 13
  8. 2022Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentationcited by 6
  9. 2025MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attentioncited by 3
  10. 2024Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studiescited by 0