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.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (No.62002318), Key Research and Development Program of Zhejiang Province (2020C01023), Zhejiang Provincial Science and Technology Project for Public Welfare (LGF21F020020), Ningbo Natural Science Foundation 202003N4318), the Fundamental Research Funds for the Central Universities (2021FZZX001-23), and the Major Scientific Research Project of Zhejiang Lab (No. 2019KD0AC01).
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Hu, J., Feng, Z., Mao, Y., Lei, J., Yu, D., Song, M. (2021). A Location Constrained Dual-Branch Network for Reliable Diagnosis of Jaw Tumors and Cysts. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_68
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