Abstract
Computer-aided diagnostics (CAD) based on deep learning methods have grown to be the most concerned method in recent years due to its safety, efficiency and economy. CAD’s function varies from providing second opinion to doctors to establishing a baseline upon which further diagnostics can be conducted [3]. In this paper, we cross-compare different approaches to classify thyroid nodules and finally propose a method that can exploit interaction between segmentation and classification task. In our method, detection and segmentation results are combined to produce class-discriminative clues for boosting classification performance. Our method is applied to TN-SCUI 2020, a MICCAI 2020 challenge and achieved third place in classification task. In this paper, we provide exhaustive empirical evidence to demonstrate the applicability and efficacy of our method.
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Shen, X., Ouyang, X., Liu, T., Shen, D. (2021). Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science(), vol 12587. Springer, Cham. https://doi.org/10.1007/978-3-030-71827-5_19
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