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ModuLab DLC-Medical1
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Introduction to Medical Image Analysis and Processing
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ModuLab DLC-Medical1
1.
2018. 10. 17. 襭
覿 覦 豌襴 - Introduction to Medical Image Analysis and Processing -
3.
(1) Dataset v 一危
(豢豌: 襭蠍郁) 則 IRB (Institutional Review Board, ) 豬渚 蟆曙 郁規 : 襭螻 , 襭蠍郁 讌, 渚 讌, 蟲一 則 螳語覲 覲危碁 (碁 覦豢 X) v Open Datasets (e.g. Grand Challenges in Biomedical Image Analysis) 襯碁襯危 螳 燕 覯覈 覲願 1947 1964 1979 1987 2004 渚 蟯襴蠍一 覈 るΜ 覦 蟯 覯襯
4.
(2) Research 則 レ(Prospective
Study) vs レ 郁規(Retrospective Study) 則 螻牛 蠏 vs 蠏 Impact Factor
5.
Why Important? Medical images
account for at least 90% of all medical data! The largest data source in the health-care industry
6.
Object Detection Semantic Segmentation Visual Tracking Action Recognition Captioning Question Answering Image Classification
7.
(1) Dataset Cleaning
(80%) Raw dataset Trainable dataset e.g. ROI (Segment), Outlier 蟇 (2) Class Imbalance Check Statistical Data Visualization
8.
(3-1) Dataset Splitting 則
Train : Validation : Test (; View of Machine Learning Researcher) = Train : Test : Extra Validation (; View of Clinician) 則 Class Imbalance 螻! 譬1 (n=600) 譬2 (n=240) ex) Train : Validation : Test = 4 : 1 : 1 譬3 (n=240) 400 160 160 100 40 40 Train Validation Test 100 40 40
9.
(3-2) Dataset Splitting 則
レ(Prospective Study) ex) (12~18) Train : Validation : (19) Test = 4 : 1 : 1 則 レ 郁規(Retrospective Study) ex) (1) Train : Validation : (2) Test = 4 : 1 : 1 2-1) Test 一危一 襦 覿襴 覈語 . (1螳 set) 2-2) Test 一危一 覦蠑語願覃 覈語 . (6螳 set) 1-1) Train : Validation 一危一 襦 覿襴 . (1螳 set) 1-2) Train : Validation 一危一 覦蠑語願覃 . (5螳 set) 1螳 豕 覈 覲旧螳 覈碁れ 蠏
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