'18 SNUH Coursework

1. 2018. 05. 10.
의용전자연구실
박사과정 이동헌
인공지능을 위한
딥러닝 프로그래밍
<AI in Medicine>

2. Week3
Machine Learning
(18. 04. 12. 13:00-17:00)
Week4
Deep Learning
(18. 05. 03. 13:00-17:00)
Week5
AI in Medicine
(18. 05. 10. 13:00-17:00)
• Introduction to AI
• Machine Learning
Overview
• Image Classification
Pipeline
• Loss functions and
Optimization
• Neural Network and
Backpropagation
• Training Neural
Networks
• Convolutional Neural
Networks (CNNs)
• CNNs Models
• Applications of CNNs
• Recurrent Neural
Networks (RNNs)
• Deep Learning in
Practice
• Applications in Medicine

3. 3
X1
X2
Xn
h1
h2
hn+1
y
w11
w(n+1)n
w21
w22
w12
wy1
wy2
wy(n+1)
h1 = f(w11X1 + w12X2 + w13X3)
h2 = f(w21X1 + w22X2 + w23X3)
…
hn+1 = f(w(n+1)1X1 + w(n+1)2X2 + w(n+1)3X3)
y = f(wy1h1 + wy2h2 + … + wy(n+1)hn+1)
Loss = D(y, Y)
Neural Network and Backpropagation

4. Training Neural Network

5. Convolutional Neural Networks

6.  Applications of CNNs
 Recurrent Neural Networks
 Deep Learning in Practice
 Applications in Medicine

7. • Detection
• Segmentation
• Understanding CNNs
• Autoencoder
• Generative Adversarial Networks

8. Detection

10. 10

11. Segmentation
• Semantic Segmentation• Semantic Segmentation
• Instance Segmentation

12. Segmentation
• Semantic Segmentation

13. epoch 10 epoch 50 epoch 70 epoch 100
Segmentation
Cervical Vertebrae Segmentation in X-ray

14. U-Net Modified U-Net U-Net Modified U-Net

15. Segmentation
• Instance Segmentation

16. Segmentation
• Instance Segmentation

17. Understanding CNNs
http://gradcam.cloudcv.org/classification

18. Understanding CNNs
Bone Age Assessment

19. Autoencoders
• Dimensionality Reduction
• Denoising
https://github.com/hwalsuklee/tensorflow-mnist-VAE

20. Autoencoders
• MIT-BIH arrhythmia database
• INCART
• SVDB
ECG Signal Denoising

21. Generative Adversarial Networks
“Generative Adversarial Network is
the most interesting idea in the last ten years in machine learning”
- Yann LeCun, Director, Facebook AI

22. Generative Adversarial Networks
Linearity
• Principle Component Analysis (PCA)

23. Generative Adversarial Networks
Nonlinearity

24. Generative Adversarial Networks
https://phillipi.github.io/pix2pix/
Style Transfer

25. Generative Adversarial Networks
https://github.com/junyanz/CycleGAN
https://github.com/hwalsuklee/tensorflow-generative-model-collections
Style Transfer
https://github.com/SKTBrain/DiscoGAN

26. 26
Generative Adversarial Networks

27. Super Resolution
Generative Adversarial Networks

28. Super Resolution
Generative Adversarial Networks

29. Generative Adversarial Networks
Super Resolution

31. • 순차적인 정보를 처리.
• 시간 스텝 단위의 출력 결과는 이전 계산 결과로부터 영향을 받음.
• 음성 인식, 텍스트, 번역, 비디오, 이미지 캡셔닝 등
Recurrent Neural Networks

32. • 입력 x, 출력 o, Hidden State h, 파라미터 U, V, W 로 구성.
• 모든 시간 스텝에 대해 파라미터 값을 공유 U, V, W
→ Backpropagation Through Time (BPTT)로 학습
→ (+) 학습해야 하는 파라미터의 수가 CNN에 비해 상대적으로 적음.
• h𝑡 는 네트워크의 메모리, 과거 시간에 일어난 일들에 대한 정보를 누적하여 기억.
→ (-) 출력값 𝑜𝑡는 현재 시간 t의 메모리에 의존하여 먼 과거에 대해서 반영이 어려움.

35. • Long-Term Dependency 문제

36. Long Short Term Memory (LSTM)

37. cell state gates

39. Outputt+1
OutputtOutputt-1
Inputt-1 Inputt
Inputt+1

40. Outputt+1
OutputtOutputt-1
Hidden
state
Inputt-1 Inputt
Inputt+1

41. Outputt+1
OutputtOutputt-1
Hidden
state
Forget
gate
Inputt-1 Inputt
Inputt+1

42. Outputt+1
OutputtOutputt-1
Hidden
state
Forget
gate Input
gate
Inputt-1 Inputt
Inputt+1

43. Outputt+1
OutputtOutputt-1
Hidden
state
Forget
gate Input
gate Output
gate
Inputt-1 Inputt
Inputt+1

44. Cell state
Outputt+1
OutputtOutputt-1
Hidden
state
Forget
gate Input
gate Output
gate
Inputt-1 Inputt
Inputt+1

45. Step1. Forget Gate Layer
Step2. Input Gate Layer

46. Step4. Output Gate Layer
Step3. Update

53. https://quickdraw.withgoogle.com/
e.g. Image Recognition
Image → Word

54. e.g. Image Captioning
Image → Sequence of words

55. Image Captioning

58. word2vec

59. word2vec

60. word2vec

61. word2vec

62. word2vec

63. word2vec

64. word2vec

66. e.g. Sentiment Classification
Sequence of words → Sentiment

67. Sentiment Classification
출처: 구글 이미지

68. e.g. Machine Translation
Sequence of words → Sequence of words

69. Machine Translation

73. e.g. Video Classification on frame Level

74. Video Classification on frame Level

75.  Data Augmentation
 Data Imbalance
 Transfer Learning
 Convolution Small Filters
 Implementation CPU-GPU
 Deep Learning Frameworks
 Limitations of Deep Learning

76. Data Augmentation

77. Data Augmentation

78. Data Augmentation

79.  Change Performance Metric
• Cohen’s Kappa
• ROC Curve
 Resampling
• Undersampling (a lot data)
• Oversampling
- Weighted Cross-entropy
- SMOTE, ADASYN and so on
 Penalized classification imposes an additional cost on the model
 Ensemble
 etc
79
Data Imbalance

80. Transfer Learning

81. Transfer Learning

82. Transfer Learning

83. Transfer Learning

84. 84
Natural Language Datasets
• 4.4 million narrative radiology reports from Stanford
• 1 million narrative radiology reports from 3 other institutions
• 430,000 radiology reports identified as normal by the interpreting radiologist
• 110,000 chest CT reports annotated for presence/absence of pulmonary
embolism
• 150 chest CT reports with all concepts annotated
Imaging Datasets
• 1000 ICU chest radiographs
• 831 bone tumor radiographs annotated by an expert radiologist with 18
features and the pathologic diagnosis
• 4000 digital mammograms annotated with 13 quality attributes
• 4000 pediatric hand radiographs with radiologist bone age
• Soon: 4.4 million Stanford exams, each with a narrative report
Transfer Learning (Open Datasets)
Medical ImageNet

85. 85
Transfer Learning (Open Datasets)
MURA

86. 86
Transfer Learning (Open Datasets)

87. 87
Transfer Learning (Open Datasets)

88. 88
Transfer Learning (Open Datasets)

89. Convolution Small Filters
The power of small filters

90. The power of small filters
Convolution Small Filters

91. Implementation (CPU-GPU)
 Software
: NVIDIA 제공의 개발 도구 및 라이브러리
• 딥러닝 기초요소 (cuDNN)
• 딥러닝 추론 엔진 (TensorRT)
• 선형 대수 (cuBLAS)
• 다중 GPU 커뮤니케이션 (NCCL)
• etc
 Hardware

92. Implementation (CPU-GPU)

93. Implementation (CPU-GPU)

94. Floating Point Precision
Implementation (CPU-GPU)

95. 95
Deep Learning Frameworks

96. Deep Learning Frameworks

97. 97
Limitations of Deep Learning

98. Limitations of Deep Learning

99. Limitations of Deep Learning
Uncertainty

100. 100
Limitations of Deep Learning
Adversarial Attack

101. • Ophthalmology
• Dermatology
• Pathology
• Cardioloy
• Neurosurgery
• EMR

102. A Survey on Deep Learning in Medical Image Analysis

103. Ophthalmology (1)
• Technology enabled by the steam engine, the electricmotor, the
microprocessor, the Internet, and many other scientific and engineering
breakthroughs has long been replacing humans in jobs that mostly involve
objective and routine tasks. … In this regard, AI is no difference, ...
• AI in Ophthalmology
① 망막(retina) 이미지
• 널리 이용가능하고 쉽게 구할 수 있으며, 눈 질병 진단을 위해 잘 정의된 기준이 있음.
• OCT 망막 이미지를 통해 나이와 관련된 황반변성과 당뇨성 망막증 분류.
② 안저(Fundus) 이미지
• 녹내장, 황반 부종 평가 및 심장 발장 및 뇌졸증 예측.
• Clinical validation of performance is one of the hurdles.
e.g 설명가능성(explainability)
→ 중요한(Salient) 이미지 특징(e.g. 혈관구조 패턴 → 나이, 혈압, 흡연 여부 예측)
• FDA 승인 (추진 중): Arterys cardio DL, Segments MRI images of the heart, and an algorithm for
the detection of diabetic retinopathy
• 알고리즘을 임상 정보와 함께 작동하도록 설계하는 것이 중요함.

104. Ophthalmology (2)
 Needs
• 당뇨성 망막병증(Diabetic Retinopathy)은 당뇨병력이 30년 이상인 환자의
90%에게 발병하게 되며, 세계 각국에서 실명의 주요 원인이 됨. 세계적으로
빠르게 증가, 약 415M명의 환자들이 위험군에 속함.
<당뇨성 망막병증의 진단을 위하여 찍은 안저 사진의 예시>
(A) 건강한 환자 (B) 당뇨성 망막병증 환자
 Objective
• 안저 이미지를 이용하여 당뇨성 망막변증 분류.

105.  Methods
• Inception-v3
 Datasets
• 후향적으로 128,175개의 안저 이미지 (Multiple grades)
① 당뇨성 qulity망막병증(Diabetic Retinopathy)
② 황반부종(Diabetic Macular Edema)
③ 이미지 quality
• 안과전문의들에게 3-7회 판독 받은 데이터
• 미국의 안과 전문의 54명이 2015년 3월부터 12월까지 참여
 Validation
• 오픈 안저 이미지 데이터셋에 검증
① EyePACS-1: 4,997명의 환자들로부터 9,963개의 이미지
② Messidor-2: 874명의 환자들로부터 1,748개의 이미지
• 인공지능 결과와 우수한 안과전문의들 7-8명의 판독 결과 비교
(개발에 참여했던 54명의 의사 중에 일관된 판독을 보여준 일부 의사가 참여)

106.  Results
• 매우 우수한 성능 (① EyePACS-1, AUC=0.991 ② Messidor-2, AUC=0.990)

107. Voets, Mike, Kajsa Møllersen, and Lars Ailo Bongo. "Replication study: Development and validation of deep learning
algorithm for detection of diabetic retinopathy in retinal fundus photographs." arXiv preprint arXiv:1803.04337 (2018).
Ophthalmology (3)
https://github.com/mikevoets/jama16-retina-replication
 Objective
• "Development and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs." Jama 316.22 (2016): 2402-2410. 재현
 Methods & Results
• Multiple grades per image → one grade per image (Acc 36% 하락)
• (Different) EyePACS AUC: 0.74 → 0.94 (after preprocessing)
• (Different) Messidor2 AUC: 0.59 → 0.82 (after preprocessing)
 Discussion
• Original study에서 자세한 설명이 부족하여 재현이 어려움.
→ Hyper-parameter setting, optimization function
→ Preprocessing method
→ Ungradable image 에 대한 알고리즘 설명 x
→ Main 알고리즘에 대한 설명만 있음
→ etc

108. Dermatology
 Needs
• 피부암은 매년 미국에서만 540만 명의 신규 환자
가 발생할 정도로 빈번한 질병.
• 피부암은 조기 발견이 중요하지만 초기에 자각
증상이 없는 경우가 많고, 피부에 있는 다른 점,
검버섯, 사마귀와 구분이 어려움.
• 흑색종의 자가진단 방법으로는 ABCDE법 권장함.
• Asymmetry: 대칭적인지
• Border : 경계가 명확한지
• Color: 색깔이 단일한지, 흰색이나 파란색이 섞여 있지 않은지
• Diameter: 직경이 6mm를 넘지 않는지
• Evolving: 점의 크기나 모양, 색깔이 시간이 지남에 따라서 변화
하고 출혈이 생기지 않는지
 Objective
• 이미지를 이용하여 피부암 종류 분류.

109.  Methods
• ‘Inception-v3’
 Datasets
• 스탠퍼드 병원, 에든버러 대학의 이미지 라이브
러리 및 인터넷에서 수집.
• 이 병변의 이미지들은 각도, 배율, 밝기 등이 각
각 다르며, 스탠퍼드 대학병원 피부과 전문의들
과의 협업을 통해서 판독함.
• 체계적으로 질병명을 지정해야 학습과 검증에
용이하므로, 다양한 피부과 질병의 계층 관계
(taxonomy)를 정의하였고, 그 결과 2,000개 이
상의 질병으로 체계적으로 판독되어 있는 약
13만 장의 피부 병변 이미지 데이터를 만듦.

110.  Validation
• 생검과 조직검사를 통해 기존에 확진한 이미지 데이터 사용.
• 의학적으로 구분이 중요한 세 가지 경우에 대해서 테스트를 진행.
① 표피세포 암 vs 지루각화증 (135장, 707장)
② 성 흑색종과 vs 양성 병변 (표준 이미지 데이터 기반 130장, 225장)
③ 성 흑색종과 vs 양성 병변 (더마토스코프로 찍은 이미지 기반 111장, 1,010장)
 Results
• 매우 우수한 성능 (① AUC=0.96 ② AUC=0.94 ③ AUC=0.91)
• 인공지능보다 정확성이 떨어지는 전문의 상당수 (의사들 성적의 평균: 초록색)

111. Pathology
 Methods
 Objective
• 림프 노드 전이를 찾는 딥러닝 알고리즘들과 전문의의 성능 비교.
• Task1: Metastasis Identification
• Task2: Whole-Slide Image Classification
 Datasets
• Challenge Competition (CAMELYON ‘16)
• Train (n=270 images) / Test (n=129 slides and images)

112.  Results
• 우수한 성능
• 전문의 (시간 제한 있음/없음)

113. Cardiology (1)
 Datasets
• Zio Patch monitor1 (wearable monitor) single-lead로
Arrhythmias 측정 (Turakhia et al., 2013)
• 다른 데이터셋보다 500배 큰 규모 (Moody & Mark,
2001; Goldberger et al., 2000)
• 대략 300,000 명의 환자 중에서 30,000명의 환자 선택
• Annotations / sec (Sampling rate = 200Hz)
 Objective
• ECG를 이용하여 다음 항목 분류.
• Sinus rhythm
• Atrial Fibrillation
• 12 Arrhythmia types
 Methods
• 34-layer CNN

114.  Results
• Sequence F1 (256 samples overlap)
• Set F1 (set of arrhythmias in 30 sec)
 Validation
• 336개의 기록 (328명의 환자)
• 6명의 cardiologists.

115. Cardiology (2)
 Datasets
• 14,011명 w/ 웨어러블 심박 모니터링 기기 (Apple Watch app heart rate sensors.)
• Medical History (이전 진단, 혈액 시험 결과, 약물 치료)
 Methods
• DeepHeart (network)
• Training Methods
① Unsupervised Sequence pretraining
② Weakly supervised Heuristic pretraining
 Objective
• ECG + 병력(Medical History) 이용하여 추정.
• 당뇨 (Diabetes)
• 높은 콜레스테롤 (High cholesterol)
• 고혈압 (High blood pressure)
• 코골이 및 수면 무호흡 (Sleep apnea)

116.  Results
 Validation
• 336개 기록 (328명의 환자)
• 심박 정보만 학습시키면 결과가 좋지 않음.
• 전반적인 physical activity 정보는 예측에 중요한 역할을 하지만, 심박은 딥러닝을
이용하기에 중요한 biomarker임.

117. Neurosurgery
 Objective
• 이미지를 보거나 상상할 때, fMRI
activity에서 구한 decoded features
를 통해, 보거나 상상한 이미지를 생
성/복원.
 Methods
• Image presentation experiments
: ① 일반 이미지 ② 기하하적인 모양 ③ 알파벳
• Imagery experiment
: 25개 이미지 (10개 일반 이미지, 15개 기하하적인 모양)
• 피험자 1 (남성, 33세), 피험자 2 (남성, 23세), 피험자 3 (여성, 23세)

118. Neurosurgery
 Objective
• 이미지를 보거나 상상할 때, fMRI
activity에서 구한 decoded features
를 통해, 보거나 상상한 이미지를 생
성/복원.
 Methods
• Image presentation experiments
: ① 일반 이미지 ② 기하하적인 모양 ③ 알파벳
• Imagery experiment
: 25개 이미지 (10개 일반 이미지, 15개 기하하적인 모양)
• 피험자 1 (남성, 33세), 피험자 2 (남성, 23세), 피험자 3 (여성, 23세)
VGG19
"Generic decoding of seen and imagined objects using hierarchical
visual features." Nature communications 8 (2017): 15037.
(Option) "Synthesizing the preferred inputs
for neurons in neural networks via deep
generator networks." Advances in Neural
Information Processing Systems. 2016.

120. Electronic Medical Record (EMR)
 Objective
• 딥러닝을 이용한 EMR 분석 방법 정리.

121.  Methods

123. 1. (p3)
• Andrew L. Beam, machine learning and medicine, Deep Learning 101 - Part 1: History and Background,
https://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html
• Abedini, R., et al. "The prediction of undersaturated crude oil viscosity: An artificial neural network and fuzzy
model approach." Petroleum Science and Technology 30.19 (2012): 2008-2021.
2. (p4)
• AIRI 400, “Machine Learning 기초” (이광희 박사)
• Standford, CS231n, http://cs231n.stanford.edu/
• Deniz Yuret’s Homepage, “Alec Radford's animations for optimization algorithms”,
http://www.denizyuret.com/2015/03/alec-radfords-animations-for.html
3. (p5)
• the data science blog, https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
• Medium, “Neural Network Architecture”, https://towardsdatascience.com/neural-network-architectures-
156e5bad51ba
4. (p8-9) Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal
networks." Advances in neural information processing systems. 2015.
5. (p10) Cireşan, Dan C., et al. "Mitosis detection in breast cancer histology images with deep neural
networks." International Conference on Medical Image Computing and Computer-assisted Intervention. Springer,
Berlin, Heidelberg, 2013.
6. (p11, p15-16) He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE,
2017.

124. 7. (p12) Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image
segmentation." International Conference on Medical image computing and computer-assisted intervention.
Springer, Cham, 2015.
8. (p17) Selvaraju, Ramprasaath R., et al. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based
Localization." ICCV. 2017.
9. (p18) Lee, Hyunkwang, et al. "Fully automated deep learning system for bone age assessment." Journal of digital
imaging 30.4 (2017): 427-441.
10. (p19) Medium, Deep inside: Autoencoders, https://towardsdatascience.com/deep-inside-autoencoders-
7e41f319999f
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