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.
Abstract (translated)
癌症的早期检测可以帮助改善患者的预后，通过早期干预。头颈部癌在专业中心进行手术活检后才能确诊，然而，这些病变有可能会被忽视，导致延迟诊断。为了克服这些挑战，我们提出了一个基于注意力的管道，该管道通过识别可疑的病变、段和分类它们为非多能、多能和肿瘤性病变来确定。我们提出了（a）基于Mask R-CNN网络的病变检测和分割视觉Transformer，以及（b）基于Multi Instance Learning（MIL）的分类方案。当前结果表明，分割模型在未见过的外部测试数据上的分割掩码和边界框准确度分数达到82%，超过了 review 的分割基准。接下来，在内部队列测试集中的分类F1分数为85%。已经开发了一个通过智能手机进行病变分割的应用程序。未来的工作包括利用内窥镜视频数据进行精确的早期检测和预后。
URL
https://arxiv.org/abs/2405.01937
