1. 国立がん研究センター,“国立がん研究センターがん情報サービス「がん統
計」（全国がん罹患モニタリング集計（MCIJ）, https://ganjoho.jp/reg_sta
t/statistics/data/dl/index.html#anchor1
(2022 年 12 月 12 日アクセス)
2. 国立がん研究センター,“生存率公表｜国立がん研究センター (ncc.go.jp),”
https://www.ncc.go.jp/jp/information/pr_release/2019/0808_1/index.html
(2022 年 12 月 12 日アクセス)
3. Harada-Shoji, Narumi, et al. "Evaluation of adjunctive ultrasonography for breast cancer detection among women aged 40-49 years w
ith varying breast density undergoing screening mammography: a se
condary analysis of a randomized clinical trial." JAMA network ope
n 4.8 (2021): e2121505-e2121505.
4. 認定 NPO 法人 J.POSH,“日本乳がんピンクリボン運動, ”
https://www.j-posh.com/cancer/early_detection/
(2022 年 12 月 12 日アクセス)
5. 厚生労働省,“2019 年 国民生活基礎調査の概況, ”
https://www.mhlw.go.jp/toukei/saikin/hw/k-tyosa/k-tyosa19/index.html
(2022 年 12 月 12 日アクセス)
6. 国立がん研究センター,“がん検診受診率 (国民生活基礎調査による推定値),
”https://ganjoho.jp/reg_stat/statistics/stat/screening/screening.html
(2022 年 12 月 12 日アクセス)
7. Haneuse, Sebastien, et al. "Mammographic interpretive volume and diagnostic mammogram interpretation performance in community
practice." Radiology 262.1 (2012): 69.
8. Chan, Heang-Ping, Ravi K. Samala, and Lubomir M. Hadjiiski. "
CAD and AI for breast cancer—recent development and challenges.
" The British journal of radiology 93.1108 (2019): 20190580.
9. Keen, John D., Joanna M. Keen, and James E. Keen. "Utilization of computer-aided detection for digital screening mammography in
the United States, 2008 to 2016." Journal of the American College
of Radiology 15.1 (2018): 44-48.
10. McKinney, Scott Mayer, et al. "International evaluation of an AI
system for breast cancer screening." Nature 577.7788 (2020): 89-94.
43
11. Kim, Hyo-Eun, et al. "Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospecti
-ve, multireader study." The Lancet Digital Health 2.3 (2020): e138e148.
12. Pacilè, Serena, et al. "Improving breast cancer detection accuracy
of mammography with the concurrent use of an artificial intelligence tool." Radiology: Artificial Intelligence 2.6 (2020).
13. Salim, Mattie, et al. "External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening
mammograms." JAMA oncology 6.10 (2020): 1581-1588.
14. Leibig, Christian, et al. "Combining the strengths of radiologists a
nd AI for breast cancer screening: a retrospective analysis." The Lan
cet Digital Health 4.7 (2022): e507-e519.
15. (社) 日本医学放射線学会, 日本放射線学術学会 (2014) マンモグラフィ
ガイドライン第 3 版 増補版, 医学書院.
16. Abdelrahman, Leila, et al. "Convolutional neural networks for breast cancer detection in mammography: A survey." Computers in biolo
gy and medicine 131 (2021): 104248.
17. Oza, Parita, et al. "Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey." Neural Computing an
d Applications (2022): 1-22.
18. Hassan, Nada M., Safwat Hamad, and Khaled Mahar. "Mammogram breast cancer CAD systems for mass detection and classificatio
n: a review." Multimedia Tools and Applications (2022): 1-33.
19. Ribli, Dezső, et al. "Detecting and classifying lesions in mammograms with deep learning." Scientific reports 8.1 (2018): 1-7.
20. Jung, Hwejin, et al. "Detection of masses in mammograms using
a one-stage object detector based on a deep convolutional neural network." PloS one 13.9 (2018): e0203355.
21. Agarwal, Richa, et al. "Deep learning for mass detection in full field digital mammograms." Computers in biology and medicine 121 (2
020): 103774.
22. Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.
44
23. Redmon, Joseph, et al. "You only look once: Unified, real-time ob
-ject detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
24. Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detec
-tion with region proposal networks." Advances in neural information
processing systems 28 (2015).
25. Heath, Michael, et al. "Current status of the digital database for
screening mammography." Digital mammography . Springer, Dordrec
ht, 1998. 457-460.
26. Moreira, Inês C., et al. "Inbreast: toward a full-field digital mammographic database." Academic radiology 19.2 (2012): 236-248.
27. Halling-Brown, Mark D., et al. "The oncology medical image database (OMI-DB)." Medical Imaging 2014: PACS and Imaging Informat
ics: Next Generation and Innovations. Vol. 9039. SPIE, 2014.
28. Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision . 201
7.
29. Agarwal, Richa, et al. "Automatic mass detection in mammograms
using deep convolutional neural networks." Journal of Medical Imaging 6.3 (2019): 031409.
30. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao.
"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for rea
l-time object detectors." arXiv preprint arXiv:2207.02696 (2022).
31. Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (201
0): 1345-1359.
32. Lin, Tsung-Yi, et al. "Microsoft coco: Common objects in context.
" European conference on computer vision. Springer, Cham, 2014.
33. Miller, Harold. "The FROC curve: A representation of the observer's performance for the method of free response." The Journal of the
Acoustical Society of America 46.6B (1969): 1473-1476.
34. Fan, Ming, et al. "Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural
network." Methods 166 (2019): 103-111.
45
35. daf, A., Crystal, P., Scaranelo, A., and Helbich, T. (2011) ”Performance of computeraided detection applied to full-field digital mammography in detection of breast cancers” Eur. journal radiology, 7
7, 457/461.
36. Tang, Jinshan, et al. "Computer-aided detection and diagnosis of breast cancer with mammography: recent advances." IEEE transactions
on information technology in biomedicine 13.2 (2009): 236-251.
謝辞
本論文を結ぶにあたり, 終始適切かつ丁寧な御指導・御助言・御鞭撻を賜る
とともに, 学生生活におきましても並々ならぬ御配慮を賜りました, 東北大学
大学院医学系研究科医用画像工学分野 本間経康教授に心より御礼申し上げま
す.
本論文作成にあたり, 副査ならびにアドバイザー教員として多大な御助言,
御配慮を賜りました東北大学大学院医学系研究科放射線検査学分野 千田浩一
教授に深く感謝申し上げます.
本研究を遂行するにあたり, 幾度にもわたる御指導・御鞭撻を下さり, 研究
室生活におきましても御配慮を賜りました, 東北大学大学院医学系研究科医用
画像工学分野 張暁勇特任准教授に厚く御礼申し上げます.
日頃より多大な御指導・御助言を下さり, 研究室生活におきましても御配慮
を賜りました, 東北大学大学院医学系研究科医用画像工学分野 市地慶講師に深
く御礼申し上げます.
また, 研究室生活におきまして, 同じ研究グループとして日頃から貴重な御
意見・御討論を頂きました, 東北大学大学院医学系研究科医用画像工学分野の
皆様並びに東北大学大学院工学系研究科杉田研究室の皆様に心より御礼申し上
げます.
最後に, 経済面・精神面で研究生活を支えていただきました家族に心より感
謝いたします.
46
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