Simulation of Postmarket Fine-tuning of a Computer-aided Detection System for Bone Scintigrams and Its Performance analysis

Author:

Shimada Kaho1,Daisaki Hiromitsu2,Higashiyama Shigeaki3,Kawabe Joji3,Nakaoka Ryusuke4,Shimizu Akinobu1

Affiliation:

1. Institute of Engineering, Tokyo University of Agriculture and Technology

2. Department of Radiological Technology, Gunma Prefectural College of Health Sciences

3. Department of Nuclear Medicine, Graduate School of Medicine, Osaka Metropolitan University

4. Division of Medical Devices, National Institute of Health Sciences

Publisher

Japanese Society for Medical and Biological Engineering

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering,Biomaterials,Biotechnology

Reference23 articles.

1. 1. Subbaswamy A, Saria S: From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 21(2), 345–352, 2020.

2. 2. Nomura Y, Masutani Y, Hayashi N, Miki S, Nemoto M, Hanaoka S: Additional learning of CAD software based on multicenter trial in teleradiology environment. Int J Comput Assist Radiol Surg. Suppl 1, S270–S271, 2012.

3. 3. Pan SJ, Yang Q: A survey on transfer learning. IEEE Trans Knowledge Data Eng. 22(10), 1345–1359, 2009.

4. 4. Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, Brink JA: Continuous learning AI in radiology: implementation principles and early applications. Radiology. 297(1), 6–14, 2020.

5. 5. Gibson E, Hu Y, Ghavami N, Ahmed HU, Moore C, Emberton M, Huisman HJ, Barratt DC: Inter-site variability in prostate segmentation accuracy using deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 506–514, 2018.

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