A Novel Multi-task Framework with Super-Resolution Directed Network for Thyroid Nodule Segmentation in Ultrasound Images
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5504-2_60
Reference14 articles.
1. Inan, N.G., Kocadağlı, O., Yıldırım, D., Meşe, İ, Kovan, Ö.: Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach. Comp. Methods and Programs in Biomedicine 243, 107921 (2024)
2. Wu, X., et al.: A knowledge-interpretable multi-task learning framework for automated thyroid nodule diagnosis in ultrasound videos. Med. Image Anal. 91, 103039 (2024)
3. Lu, Z., She, C., Wang, W., Huang, Q.: LM-Net: A light-weight and multi-scale network for medical image segmentation. Comput. Biol. Med. 168, 107717 (2024)
4. Sujini Ganne, N., Balakrishna, S.: Categorization of Thyroid Cancer Sonography Images Using an Amalgamation of Deep Learning Techniques. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds.) Soft Computing and Signal Processing. ICSCSP 2022. Smart Innovation, Systems and Technologies, vol 313. Springer, Singapore (2013). https://doi.org/10.1007/978-981-19-8669-7_42
5. Zheng, T., et al.: Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture. BMC Med. Imaging 23(1), 56 (2023)
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