{"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-the-prediction-of-covid-19","title":"One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features","arxiv_id":null,"date":"2021-01-04","proceeding":null,"authors":["Aram Ter-Sarkisov"],"abstract":"We introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation\r\nof an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the\r\naffinity between the lesion masks’ features extracted from the image. First, a batch of vectorized lesion masks is\r\nconstructed. Then, the model learns the parameters of the affinity matrix that captures the relationship between features\r\nin each vector. Finally, the affinity is expressed as a single vector of pre-defined length. Without any complicated data\r\nmanipulation, class balancing tricks, and using only a fraction of the training data, we achieve a 91.74% COVID-19\r\nsensitivity, 85.35% common pneumonia sensitivity, 97.26% true negative rate and 91.94% F1-score. Ablation studies show\r\nthat the method can quickly generalize to new datasets. All source code, models and results are publicly available on\r\nhttps://github.com/AlexTS1980/COVID-Affinity-Model.","url_abs":"https://www.medrxiv.org/content/10.1101/2020.12.29.20248987v1.full.pdf","url_pdf":"https://www.medrxiv.org/content/10.1101/2020.12.29.20248987v1.full.pdf","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-the-prediction-of-covid-19","repo_url":"https://github.com/AlexTS1980/COVID-Affinity-Model","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":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"transposed-convolution","method_name":"Transposed convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}