{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/one-shot-model-for-covid-19-classification","title":"One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism","arxiv_id":null,"date":"2021-02-19","proceeding":null,"authors":["Aram Ter-Sarkisov`"],"abstract":"We present a model that fuses instance segmentation, Long Short-Term Memory Network and Attention mechanism to predict COVID-19 and segment chest CT scans. The model works by extracting a sequence of Regions of Interest that contain class-relevant information, and applies two Long Short-Term Memory networks with attention to this sequence to extract class-relevant features. The model is trained in one shot: both segmentation and classification branches, using two different sets of data. We achieve a 95.74% COVID-19 sensitivity, 98.13% Common Pneumonia sensitivity, 99.27% Control sensitivity and 98.15% class-adjusted F1 score on the main dataset of 21191 chest CT scan slices, and also run a number of ablation studies in which we achieve 97.73% COVID-19 sensitivity and 98.41% F1 score. All source code and models are available on https://github.com/AlexTS1980/COVID-LSTM-Attention.","url_abs":"https://www.medrxiv.org/content/10.1101/2021.02.16.21251754v1","url_pdf":"https://www.medrxiv.org/content/10.1101/2021.02.16.21251754v1","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"one-shot-model-for-covid-19-classification","repo_url":"https://github.com/AlexTS1980/COVID-LSTM-Attention","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"},{"task_slug":"covid-19-image-segmentation","task_name":"COVID-19 Image Segmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"memory-network","method_name":"Memory Network"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}