An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images
arxiv(2024)
摘要
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
unseen external test data and surpassing reviewed segmentation benchmarks.
Next, a classification F1-score of 85
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.
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