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.

翻译：早期检测癌症可通过早期干预改善患者预后。头颈部癌症通常在专科中心通过手术活检确诊，但存在漏诊风险，导致诊断延迟。为克服这些挑战，我们提出了一种基于注意力的管线，能够识别可疑病变、分割病灶，并将其分类为非异型增生、异型增生和癌变病变。我们提出：(a) 基于视觉Transformer的Mask R-CNN网络，用于临床图像的病变检测与分割；(b) 基于多实例学习（MIL）的分类方案。目前结果表明，分割模型在未见外部测试数据上生成的分割掩膜和边界框重叠准确率高达82%，超过了已审核的分割基准。此外，内部队列测试集的分类F1分数为85%。我们已开发出一款应用，可通过智能设备进行病变分割。未来工作将涉及利用内窥镜视频数据实现精准早期检测与预后评估。