Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought

• URL: http://arxiv.org/abs/2108.05649v1
• Date: Thu, 12 Aug 2021 10:34:22 GMT
• Title: Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought
• Authors: Fouzia Altaf, Syed M.S. Islam, Naveed Akhtar
• Abstract summary: Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Our critical analysis of the literature reveals an alarming performance disparity between different published results.
• Score: 20.66890710233269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. Due to the high sensitivity of Computed Tomography (CT) to this disease, CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Outcomes published in this direction are frequently claiming highly accurate detection under deep transfer learning. This is leading medical technologists to believe that deep transfer learning is the mainstream solution for the problem. However, our critical analysis of the literature reveals an alarming performance disparity between different published results. Hence, we conduct a systematic thorough investigation to analyze the effectiveness of deep transfer learning for COVID-19 detection with CT images. Exploring 14 state-of-the-art visual models with over 200 model training sessions, we conclusively establish that the published literature is frequently overestimating transfer learning performance for the problem, even in the prestigious scientific sources. The roots of overestimation trace back to inappropriate data curation. We also provide case studies that consider more realistic scenarios, and establish transparent baselines for the problem. We hope that our reproducible investigation will help in curbing hype-driven claims for the critical problem of COVID-19 diagnosis, and pave the way for a more transparent performance evaluation of techniques for CT-based COVID-19 detection.

Related papers

• An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics [0.0]
  Decision support systems relax the challenges inherent to the physical examination of images. Most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. This paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model.
  arXiv  Detail & Related papers  (2023-05-30T13:14:03Z)
• A review of Deep learning Techniques for COVID-19 identification on Chest CT images [0.0]
  The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Researchers have proven it to be highly effective for COVID-19 CT image classification.
  arXiv  Detail & Related papers  (2022-07-29T18:27:20Z)
• Challenges in COVID-19 Chest X-Ray Classification: Problematic Data or Ineffective Approaches? [0.0]
  deep learning to classify and detect COVID-19 infections from chest radiography images. In this work, we investigate the challenges faced with creating reliable AI solutions from both the data and machine learning perspectives.
  arXiv  Detail & Related papers  (2022-01-16T14:12:04Z)
• MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis [4.5497948012757865]
  We present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain. We also present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.
  arXiv  Detail & Related papers  (2021-06-14T15:48:14Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• Few-shot Learning for CT Scan based COVID-19 Diagnosis [33.26861533338019]
  Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. We propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available.
  arXiv  Detail & Related papers  (2021-02-01T02:37:49Z)
• COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
  We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2. Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
  arXiv  Detail & Related papers  (2021-01-19T03:04:09Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
  The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency. We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
  arXiv  Detail & Related papers  (2020-04-30T03:13:40Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.