Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification

Author:

Zhao Jun,Zhou Xiaosong,Shi Guohua,Xiao Ning,Song Kai,Zhao Juanjuan,Hao Rui,Li Keqin

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Taiyuan 2019-nCoV prophylaxis and treatment research project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference50 articles.

1. Acharya UR, Chowriappa P, Fujita H, Bhat S, Dua S, Koh JE, Eugene L, Kongmebhol P, Ng KH (2016) Thyroid lesion classification in 242 patient population using gabor transform features from high resolution ultrasound images. Knowl-Based Syst 107:235–245

2. Avola D, Cinque L, Fagioli A, Filetti S, Rodola E (2021) Multimodal feature fusion and knowledge-driven learning via experts consult for thyroid nodule classification. IEEE Trans Circ Syst Video Technol PP (99):1–1

3. Brock A, Donahue J, Simonyan K (2018) Large scale gan training for high fidelity natural image synthesis. In: International conference on learning representations

4. Bullinaria JA, Levy JP (2007) Extracting semantic representations from word co-occurrence statistics: a computational study. Behav Res Methods 39(3):510–526

5. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A (2017) Deep learning: a primer for radiologists. Radiographics 37(7):2113–2131

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