mlsanityData to 7 Oct 2026

A Trustworthy Framework for Medical Image Analysis with Deep Learning

arXiv 2212.02764 ↗First posted 6 Dec 2022eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

22 of 31 references.

References · 22

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10 more
  1. 2020MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Modelscited by 61
  2. 2021COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Predictioncited by 36
  3. 2021COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Imagescited by 22
  4. 2020How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networkscited by 19
  5. 2018Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filteringcited by 8
  6. 2022Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregationcited by 7
  7. 2021MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysiscited by 6
  8. 2021Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classificationscited by 4
  9. 2020Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Servicescited by 3
  10. 2022Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Reviewcited by 3

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Understanding Calibration of Deep Neural Networks for Medical Image Classificationcited by 5