Computer-aided diagnosis for early detection and staging of human pancreatic tumors using an optimized 3D CNN on computed tomography
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s00530-023-01146-2.pdf
Reference41 articles.
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2. Sinkala, M., Mulder, N., Martin, D.: Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics. Sci. Rep. 10(1), 1–14 (2020)
3. Qiu, J.J., Wu, Y., Hui, B., Huang, Z.X., Chen, J.: Texture analysis of computed tomography images in the classification of pancreatic cancer and normal pancreas: a feasibility study. J. Med. Imaging Health Inform. 8(8), 1539–1545 (2018)
4. Preuss, K., Thach, N., Liang, X., Baine, M., Chen, J., Zhang, C., Du, H., et al.: Using quantitative imaging for personalized medicine in pancreatic cancer: a review of radiomics and deep learning applications. Cancers 14(7), 1654 (2022)
5. Dumitrescu, E.A., Ungureanu, B.S., Cazacu, I.M., Florescu, L.M., Streba, L., Croitoru, V.M., Sur, D., Croitoru, A., Turcu-Stiolica, A., Lungulescu, C.V.: Diagnostic value of artificial intelligence-assisted endoscopic ultrasound for pancreatic cancer: a systematic review and meta-analysis. Diagnostics 12(2), 309 (2022)
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