PropertyValue
?:abstract
  • We introduce a lightweight model based on Mask R-CNN with ResNet18 and ResNet34 backbone models that segments lesions and predicts COVID-19 from chest CT scans in a single shot. The model requires a small dataset to train: 650 images for the segmentation branch and 3000 for the classification branch, and it is evaluated on 21292 images to achieve a 42.45% average precision (main MS COCO criterion) on the segmentation test split (100 images), 93.00% COVID-19 sensitivity and F1-score of 96.76% on the classification test split (21192 images) across 3 classes: COVID-19, Common Pneumonia and Control/Negative. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-Single-Shot-Model.
is ?:annotates of
?:creator
?:doi
  • 10.1101/2020.12.01.20241786
?:doi
?:license
  • medrxiv
?:pdf_json_files
  • document_parses/pdf_json/f37abefa45a3b46f3a9e28b84aa4ae4a0371b566.json
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • MedRxiv; WHO
?:title
  • Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans
?:type
?:year
  • 2020-12-03

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