{"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/dino-cxr-a-self-supervised-method-based-on","title":"DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification","arxiv_id":"2308.00475","date":"2023-08-01","proceeding":null,"authors":["Mohammadreza Shakouri","Fatemeh Iranmanesh","Mahdi Eftekhari"],"abstract":"The limited availability of labeled chest X-ray datasets is a significant bottleneck in the development of medical imaging methods. Self-supervised learning (SSL) can mitigate this problem by training models on unlabeled data. Furthermore, self-supervised pretraining has yielded promising results in visual recognition of natural images but has not been given much consideration in medical image analysis. In this work, we propose a self-supervised method, DINO-CXR, which is a novel adaptation of a self-supervised method, DINO, based on a vision transformer for chest X-ray classification. A comparative analysis is performed to show the effectiveness of the proposed method for both pneumonia and COVID-19 detection. Through a quantitative analysis, it is also shown that the proposed method outperforms state-of-the-art methods in terms of accuracy and achieves comparable results in terms of AUC and F-1 score while requiring significantly less labeled data.","url_abs":"https://arxiv.org/abs/2308.00475v1","url_pdf":"https://arxiv.org/pdf/2308.00475v1.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":[],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"},{"task_slug":"pneumonia-detection","task_name":"Pneumonia Detection"},{"task_slug":"self-supervised-image-classification","task_name":"Self-Supervised Image Classification"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"x-ray-classification","task_name":"X-ray Classification"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/covid-19-diagnosis-on-covidgr","task":"COVID-19 Diagnosis","dataset":"COVIDGR","model":"DINO-CXR","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"76.47"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-classification-on-covidgr","task":"Medical Image Classification","dataset":"COVIDGR","model":"DINO-CXR","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"76.47"},"uses_additional_data":false},{"leaderboard":"/sota/pneumonia-detection-on-chest-x-ray-images-1","task":"Pneumonia Detection","dataset":"Chest X-ray images","model":"DINO-CXR","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"95.65"},"uses_additional_data":false},{"leaderboard":"/sota/self-supervised-image-classification-on-chest","task":"Self-Supervised Image Classification","dataset":"Chest X-ray images","model":"DINO-CXR","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"95.66"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}