A Location Constrained Dual-Branch Network for Reliable Diagnosis of Jaw Tumors and Cysts

Abstract

The jaw tumors and cysts are usually painless and asymptomatic, which poses a serious threat to patient life quality. Proper and accurate detection at the early stage will effectively relieve patients’ pain and avoid radical segmentation surgery. However, similar radiological characteristics of some tumors and cysts bring challenges for accurate and reliable diagnosis of tumors and cysts. What’s more, existing transfer learning based classification and detection methods for diagnosis of tumors and cysts have two drawbacks: a) diagnosis performance of the model is highly reliant on the number of lesion samples; b) the diagnosis results lack reliability. In this paper, we proposed a Location Constrained Dual-branch Network (LCD-Net) for reliable diagnosis of jaw tumors and cysts. To overcome the dependence on a large number of lesion samples, the features extractor of LCD-Net is pretrained with self-supervised learning on massive healthy samples, which are easier to collect. For similar radiological characteristics, the auxiliary segmentation branch is devised for extracting more distinguishable features. What’s more, the dual-branch network combined with the patch-covering data augmentation strategy and localization consistency loss is proposed to improve the model’s reliability. In the experiment, we collect 872 lesion panoramic radiographs and 10, 000 healthy panoramic radiographs. Exhaustive experiments on the collected dataset show that LCD-Net achieves SOTA and reliable performance, which provides an effective tool for diagnosing jaw tumors and cysts.