Deep Learning for Relevant Findings in Colonoscopy
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54450-7_32
Reference19 articles.
1. Taghiakbari, M., Mori, Y., von Renteln, D.: Artificial intelligence-assisted colonoscopy: a review of current state of practice and research. World J. Gastroenterol. 27(47), 8103–8122 (2021)
2. Vulpoi, R.A., Luca, M., Ciobanu, A., Olteanu, A., Bărboi, O.B., Drug, V.L.: Artificial intelligence in digestive endoscopy-where are we and where are we going? Diagnostics (Basel) 12(4), 927 (2022). https://doi.org/10.3390/diagnostics12040927. https://pubmed.ncbi.nlm.nih.gov/35453975/. PMCID: PMC9029251
3. Intelligent Systems Reference Library;M Luca,2022
4. Advances in Intelligent Systems and Computing;M Luca,2021
5. Luca, M., Ciobanu, A.: Polyp detection in video colonoscopy using deep learning. J. Intell. Fuzzy Syst. 43(2), 1751–1759 (2022). https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs219276
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