COVID-CT-Dataset: A CT Scan Dataset about COVID-19

• URL: http://arxiv.org/abs/2003.13865v3
• Date: Wed, 17 Jun 2020 20:14:22 GMT
• Title: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• Authors: Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang, Shanghang Zhang, Pengtao Xie
• Abstract summary: During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain. We build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs.
• Score: 33.60943657492132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbreak of this pandemic. We also perform experimental studies which further demonstrate that this dataset is useful for developing AI-based diagnosis models of COVID-19. Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0.90, an AUC of 0.98, and an accuracy of 0.89. According to the senior radiologist, models with such performance are good enough for clinical usage. The data and code are available at https://github.com/UCSD-AI4H/COVID-CT

Related papers

• COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics [79.90346960083775]
  We introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than 2.66 times. We provide extensive analysis on the diversity of the patient demographic, imaging metadata, and disease distributions to highlight potential dataset biases.
  arXiv  Detail & Related papers  (2023-11-29T14:40:31Z)
• COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images [82.74877848011798]
  We introduce COVIDx CT-3, a large-scale benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries. We examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding significant geographic and class imbalances.
  arXiv  Detail & Related papers  (2022-06-07T06:35:48Z)
• HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation [0.0]
  During the COVID-19 pandemic, computed tomography (CT) was a popular method for diagnosing COVID-19 patients. Publicly accessible COVID-19 CT image datasets are difficult to come by due to privacy concerns. We have introduced HRCTCov19, a new COVID-19 high-resolution chest CT scan image dataset.
  arXiv  Detail & Related papers  (2022-05-06T12:49:18Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans [29.266579630983358]
  During pandemic era, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error. This paper introduces an open access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. A Deep Neural Network (DNN)-based framework is proposed, referred to as the COVID-Rate, that autonomously segments lung abnormalities associated with COVID-19 from chest CT scans.
  arXiv  Detail & Related papers  (2021-07-04T03:19:43Z)
• 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)
• Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19 [6.142272540492935]
  This study builds a large-size publicly available CT-scan dataset, consisting of more than 13k CT-images of more than 1000 individuals, in which 8k images are taken from 500 patients infected with COVID-19. We propose a deep learning model for screening COVID-19 using our proposed CT dataset and report the baseline results. Finally, we extend the proposed CT model for screening COVID-19 from CXR images using a transfer learning approach.
  arXiv  Detail & Related papers  (2020-12-28T11:52:33Z)
• 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)
• Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset [1.4680035572775532]
  The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients. This study aims to develop a deep learning-based model that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset.
  arXiv  Detail & Related papers  (2020-07-24T13:51:58Z)
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [49.09507792800059]
  We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
  arXiv  Detail & Related papers  (2020-05-07T06:00:02Z)

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.