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：癌症的早期检测可通过早期干预改善患者预后。头颈部癌通常在专科中心通过手术活检确诊，但存在漏诊风险从而导致诊断延迟。为应对这些挑战，我们提出了一种基于注意力机制的流水线，用于识别疑似病变、分割病灶并将其分类为非增生异常、增生异常和癌变病灶。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。当前结果显示，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%，超越了已审阅的分割基准。此外，内部队列测试集的分类F1分数达到85%。我们已开发出一款可通过智能设备拍摄病变图像进行分割的应用。未来工作将涉及使用内窥镜视频数据实现精准早期检测与预后评估。