An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images
This addresses the challenge of missed or delayed diagnoses in head and neck cancer, potentially improving patient prognosis through early intervention, though it appears incremental as it builds on existing methods like vision transformers and MIL.
The paper tackles the problem of early detection of head and neck cancer by developing an attention-based pipeline that identifies, segments, and classifies lesions from clinical images, achieving up to 82% overlap accuracy in segmentation and an 85% F1-score in classification.
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.