{"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/an-attention-based-pipeline-for-identifying","title":"An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images","arxiv_id":"2405.01937","date":"2024-05-03","proceeding":null,"authors":["Abdullah Alsalemi","Anza Shakeel","Mollie Clark","Syed Ali Khurram","Shan E Ahmed Raza"],"abstract":"Early detection of cancer can help improve patient prognosis by early intervention. Head and neck cancer is diagnosed in specialist centres after a surgical biopsy, however, there is a potential for these to be missed leading to delayed diagnosis. To overcome these challenges, we present an attention based pipeline that identifies suspected lesions, segments, and classifies them as non-dysplastic, dysplastic and cancerous lesions. We propose (a) a vision transformer based Mask R-CNN network for lesion detection and segmentation of clinical images, and (b) Multiple Instance Learning (MIL) based scheme for classification. Current results show that the segmentation model produces segmentation masks and bounding boxes with up to 82% overlap accuracy score on unseen external test data and surpassing reviewed segmentation benchmarks. Next, a classification F1-score of 85% on the internal cohort test set. An app has been developed to perform lesion segmentation taken via a smart device. Future work involves employing endoscopic video data for precise early detection and prognosis.","url_abs":"https://arxiv.org/abs/2405.01937v2","url_pdf":"https://arxiv.org/pdf/2405.01937v2.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":"an-attention-based-pipeline-for-identifying","repo_url":"https://github.com/precision-vision/oed-classification-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"lesion-detection","task_name":"Lesion Detection"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"prognosis","task_name":"Prognosis"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"convolution","method_name":"Convolution"},{"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":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"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}