Deep learning for biomedical image reconstruction: a survey

Author:

Ben Yedder HaneneORCID,Cardoen Ben,Hamarneh Ghassan

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

CIHR Skin Research Training Centre

Nvidia

GDES/SFU

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference156 articles.

1. Adler J, Öktem O (2018) Learned primal-dual reconstruction. IEEE Trans Med Imaging 37(6):1322–1332

2. Aghasi A, Abdi A, Nguyen N, Romberg J (2017) Net-trim: convex pruning of deep neural networks with performance guarantee. In: Advances in neural information processing systems, pp 3177–3186

3. Alford S, Robinett R, Milechin L, Kepner J (2018) Pruned and structurally sparse neural networks. arXiv preprint arXiv:1810.00299

4. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292

5. Al-Shakhrah I, Al-Obaidi T (2003) Common artifacts in computerized tomography: a review. Appl Radiol 32(8):25–32

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