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컴퓨터비전, 머신러닝, 딥러닝을 이용한 의료영상분석
박상현 교수
DGIST
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컴퓨터비전, 머신러닝, 딥러닝을 이용한 의료영상분석
컴퓨터비전, 머신러닝, 딥러닝을 이용한 의료영상분석
http://www.edwith.org/medical-20200327/lecture/63135
좋아요
1304
수강생
4053
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하위 메뉴
강의목록
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1. Introduction to medical image analysis
1.Overview
2.Introduction to medical image analysis 1
3.Introduction to medical image analysis 2
4.PACS/DICOM/Visualization
5.Image acquisition
6.X-ray / CT / PET
7.Magnetic Resonance Imaging (MRI)
Quiz 1
2. Medical image classification(1)
1.Introduction to medical image classification
2.Linear Regression
3.Logistic Regression
4.Neural Network
5.Image Classification
6.Medical image classification
7.Classification with demographic scores
Quiz 2
3. Medical image classification(2)
1.Property of Deep Neural Network
2.Convolution
3.Convolutional Neural Network (CNN)
4.Advanced CNNs (LeNet, AlexNet, VGG)
5.Advanced CNNs (ResNet, InceptionNet, DenseNet)
6.3D CNN with demographic scores
Quiz 3
4. Medical image classification(3)
1.Overall procedure
2.Validation
3.Overfitting / Regularization
4.Transfer Learning
5.Data Augmentation
6.Evaluation of classification model
7.Evaluation of classification model (Multi-label)
Quiz 4
5. Medical image classification(4)
1.Feature selection using L1 regularization
2.Feature selection using Entropy / Mutual information
3.Feature extraction using Deep Learning
4.Class Activation Map
5.Weekly supervised learning
6.Multiple instance learning
Quiz 5
6. Medical image segmentation(1)
1.Introduction to medical image segmentation
2.Otsu thresholding
3.Morphological processing
4.Region growing / Watershed algorithm
5.Segmentation using graph model
6.Graph cut optimization
Quiz 6
7. Medical image segmentation(2)
1.Active Contour Model
2.Atlas based method / Label fusion
3.Segmentation via learning based method
4.Principle Component Analysis (PCA)
5.Active shape model
6.Segmentation using classifier
Quiz 7
8. Medical image segmentation(3)
1.Fully Convolution Network(FCN)
2.U-net
3.Dilated Convolution
4.DeepLab V3+
5.Segmentation using 3D CNN
6.Loss Function
7.Segmentation Metric
Quiz 8
9. Medical image Enhancement(1)
1.Introduction to medical image enhancement
2.Intensity normalization
3.Histogram equalization
4.Histogram Matching
5.Spatial Filtering
6.Anisotropic diffusion filtering
7.Vessel enhancement filtering
Quiz 9
10. Medical image Enhancement(2)
1.Filtering in frequency domain
2.Filtering in 2D frequency domain
3.Spatial domain vs Frequency domain
4.Non-Local Mean denoising
5.Denoising with Dictionary
6.Dictionary Learning
7.Super-resolution via dictionary learning
Quiz 10
11. Medical image Enhancement(3)
1.SRCNN
2.Upsampling strategy
3.Deep networks for super resolution
4.Generative Adversarial Network(GAN)
5.SRGAN
6.CNN for medical image enhancement
7.Enhancement metric
Quiz 11
12. Medical image registration(1)
1.Introduction to medical image registration
2.Overview
3.Transformation Matrix in 2D
4.Transformation Matrix in 3D
5.Backward warping
6.Interpolation
7.Similarity measure – SSD, SAD, NCC
8.Similarity measure – Mutual information
Quiz 12
13. Medical image registration(2)
1.Registration types
2.Registration using main axis
3.Iterative Closest Point (ICP)
4.Nonrigid registration via ICP
5.Nonrigid registration via B-spline
6.Nonrigid registration via deformable model
Quiz 13
14. Medical image registration(3)
1.Optical flow / FlowNet
2.Data augmentation for optical flow
3.3D image registration via CNN
4.Spatial Transformer Network
5.3D image registration via unsupervised learning
6.Registration metric
Quiz 14
강의평가
강의평가
토론게시판
공지게시판
5.Advanced CNNs (ResNet, InceptionNet, DenseNet)
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5.Advanced CNNs (ResNet, InceptionNet, DenseNet) - 교수학습센터
5.Advanced CNNs (ResNet, InceptionNet, DenseNet) - 교수학습센터
좋아요 21
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4.Advanced CNNs (LeNet, AlexNet, VGG)
6.3D CNN with demographic scores
