Improving Classification Performance in Gastric Disease through Realistic Data Augmentation Technique Based on Poisson Blending

Author:

Lee Han-sung,Cho Hyun-chongORCID

Funder

Ministry of Education

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Reference20 articles.

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2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

3. Katai H, Ishikawa T, Akazawa K, Isobe Y, Oda I, Tsujitani S, Ono H, Tanabe S, Fukagawa T, Nunobe S, Kakeji Y, Nashimoto A, Nashimoto and Registration Committee of the Japanese Gastric Cancer Association (2018) Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer 21:144–154

4. Nam JY, Chung HJ, Choi KS, Lee H, Kim TJ, Soh H, Kang EA, Cho SJ, Ye JC, Im JP, Kim SG (2022) Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison. Gastrointest Endosc 95(2):258–268

5. Yusuke H, Toshiaki H, Naoki I, Yoshitaka T, Ken N, Shoichi Y, Akiyoshi I, Toshiyuki Y, Tomohir T, Junko F, Tomohiro T (2020) Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos). Gastrointest Endosc 92(4):856–865

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