Janne Näppi - Search Results

Year	Number of Results
2002	3
2003	1
2004	2
2005	2
2006	1
2007	2
2008	4
2009	3
2010	2
2011	3
2012	3
2013	1
2014	2
2015	3
2016	1
2017	1
2018	1
2019	1
2020	3
2021	3
2022	1
2023	3
2024	1

1

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography.

Tachibana R, Näppi JJ, Ota J, Kohlhase N, Hironaka T, Kim SH, Regge D, Yoshida H. Tachibana R, et al. Among authors: nappi jj. Radiographics. 2018 Nov-Dec;38(7):2034-2050. doi: 10.1148/rg.2018170173. Radiographics. 2018. PMID: 30422761 Free PMC article. Review.

2

Sources of false positives in computer-assisted CT colonography.

Näppi JJ, Nagata K. Näppi JJ, et al. Abdom Imaging. 2011 Apr;36(2):153-64. doi: 10.1007/s00261-010-9625-5. Abdom Imaging. 2011. PMID: 20473667 Review.

3

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

Mahmood U, Shukla-Dave A, Chan HP, Drukker K, Samala RK, Chen Q, Vergara D, Greenspan H, Petrick N, Sahiner B, Huo Z, Summers RM, Cha KH, Tourassi G, Deserno TM, Grizzard KT, Näppi JJ, Yoshida H, Regge D, Mazurchuk R, Suzuki K, Morra L, Huisman H, Armato SG 3rd, Hadjiiski L. Mahmood U, et al. Among authors: nappi jj. BJR Artif Intell. 2024 Mar 4;1(1):ubae003. doi: 10.1093/bjrai/ubae003. eCollection 2024 Jan. BJR Artif Intell. 2024. PMID: 38476957 Free PMC article. Review.

4

Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.

Iida Y, Näppi J, Kitano T, Hironaka T, Katsumata A, Yoshida H. Iida Y, et al. Among authors: nappi j. Oral Radiol. 2023 Jul;39(3):553-562. doi: 10.1007/s11282-023-00669-8. Epub 2023 Feb 8. Oral Radiol. 2023. PMID: 36753006

5

Bayesian uncertainty estimation for detection of long-tailed and unseen conditions in medical images.

Rezaei M, Näppi JJ, Bischl B, Yoshida H. Rezaei M, et al. Among authors: nappi jj. J Med Imaging (Bellingham). 2023 Sep;10(5):054501. doi: 10.1117/1.JMI.10.5.054501. Epub 2023 Oct 9. J Med Imaging (Bellingham). 2023. PMID: 37818179

6

Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study.

Tachibana R, Näppi JJ, Hironaka T, Yoshida H. Tachibana R, et al. Among authors: nappi jj. Cancers (Basel). 2022 Aug 26;14(17):4125. doi: 10.3390/cancers14174125. Cancers (Basel). 2022. PMID: 36077662 Free PMC article.

7

U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19.

Näppi JJ, Uemura T, Watari C, Hironaka T, Kamiya T, Yoshida H. Näppi JJ, et al. Sci Rep. 2021 Apr 29;11(1):9263. doi: 10.1038/s41598-021-88591-z. Sci Rep. 2021. PMID: 33927287 Free PMC article.

8

Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT.

Uemura T, Näppi JJ, Watari C, Hironaka T, Kamiya T, Yoshida H. Uemura T, et al. Among authors: nappi jj. Med Image Anal. 2021 Oct;73:102159. doi: 10.1016/j.media.2021.102159. Epub 2021 Jul 11. Med Image Anal. 2021. PMID: 34303892 Free PMC article.

9

Generative multi-adversarial network for striking the right balance in abdominal image segmentation.

Rezaei M, Näppi JJ, Lippert C, Meinel C, Yoshida H. Rezaei M, et al. Among authors: nappi jj. Int J Comput Assist Radiol Surg. 2020 Nov;15(11):1847-1858. doi: 10.1007/s11548-020-02254-4. Epub 2020 Sep 8. Int J Comput Assist Radiol Surg. 2020. PMID: 32897490 Free PMC article.

10

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Hadjiiski L, Cha K, Chan HP, Drukker K, Morra L, Näppi JJ, Sahiner B, Yoshida H, Chen Q, Deserno TM, Greenspan H, Huisman H, Huo Z, Mazurchuk R, Petrick N, Regge D, Samala R, Summers RM, Suzuki K, Tourassi G, Vergara D, Armato SG 3rd. Hadjiiski L, et al. Among authors: nappi jj. Med Phys. 2023 Feb;50(2):e1-e24. doi: 10.1002/mp.16188. Epub 2023 Jan 6. Med Phys. 2023. PMID: 36565447

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