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Applications of Deep Learning in Radiology - ESPR · Applications of Deep Learning in Radiology and...
Transcript of Applications of Deep Learning in Radiology - ESPR · Applications of Deep Learning in Radiology and...
Applications of DeepLearning in Radiology and
Pediatric Radiology
Dr. İlker Özgür KoskaAfyon SB University Pediatric Radiology
İzmir Dokuz Eylül Universtiy Biomedical Technologies
Prof Dr. Hüdaver Alper
Outline
Building blocks of deep learning
*Artificial neural networks (ANN)
*Working principle of ANN
*Fully Connected Deep Neural Networks
*Convolutional Deep Neural Networks
*Residual Deep Neural Networks
*Autoencoders
Application examples of deep learning in radiology
Artificial neural networks
Malign
0.1 0.3 0.410.2 0.7 0.150.3 0.2 0.320.4 0.3 0.80.2 0.5 0.12
M1 M2 M3
İ1İ2İ3İ4İ5
0.14 0.250.3 0.440.18 0.7
O1 O2
M1M2M3
Deep learning model
How it worksRandom initial weights
Inputs and weights are multiplied and addedthen feed the result to activation functions(feed forward)
Computing the error between the target valueand computed value
Computing how it can minimise the error bydifferentiation (gradient descent)
Computing new values of weights which willdecrease the error(back propagation)
Repeating the process till satisfiying thepredetermined stop criteria (epochs)
Gradient descent
Fully connected (dense) network
Short diameter
Spiculation ratio
Histogram mean
GLCM heterogeneity
GLRLM short run emphasis
Input layer
Hidden layer
Output layer
0 Benign
1 Malign
Convolution
1 0 1
0 1 0
1 0 1
Mask
*
What does convolution do?
-1 -1 -1
-1 5 -1
-1 -1 -1
=*
Pooling Activation functions
Convolutional neural networks
Liver
Gallbladder
Spleen..Kidney
Residual learning
U-Net
Convolutional AutoencoderAutoencoder
Sparse CT Reconstruction
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Hu Chen, Yi Zhang*, Member, IEEE, Mannudeep K. Kalra, Feng Lin, Yang Chen, Peixo Liao, Jiliu
Zhou, Senior Member, IEEE, and Ge Wang, Fellow, IEEE
QUANTITATIVE RESULTS (MEAN±SDs)
PNSR RMSE SSIM
LDCT 39.4314±1.5206 0.0109±0.0021 0.9122±0.0280
TV-POCS 41.7496±1.1522 0.0083±0.0012 0.9535±0.0143
K-SVD 42.7203±1.4260 0.0074±0.0014 0.9531±0.0167
BM3D 42.7661±1.0471 0.0073±0.0009 0.9563±0.0125
CNN10 43.6561±1.1323 0.0066±0.0009 0.9664±0.0100
KAIST-Net 43.9668±1.2169 0.0064±0.0009 0.9688±0.0110
RED-CNN 44.4187±1.2118 0.0060±0.0009 0.9705±0.0087
(a) NDCT, (b) LDCT, (c) TV-POCS, (d) K-SVD, (e) BM3D, (f) CNN10, (g)
KAIST-Net, and (h) RED-CNN
Sparse MR Reconstructionk-Space Deep Learning for Accelerated MRIYoseob Han, and Jong Chul Ye, Senior Member, IEEE
arXiv:1805.03779v1 [cs.CV] 10 May 2018
a) Image domain learning, (b) cascaded network,c) AUTOMAP d) k-space learning..
Artefact ReductionConvolutional Neural Network based Metal ArtifactReduction in X-ray Computed TomographyYanbo Zhang, Senior Member, IEEE, and Hengyong Yu*, Senior Member, IEEE
arXiv:1709.01581v2 [physics.med-ph] 20 Apr 2018
RMSE OF EACH IMAGE IN THE NUMERICAL SIMULATION STUDY. (UNIT: HU).Original BHC LI NMAR1 NMAR2 CNN CNN-MAR
Case 1 155.0 86.3 46.2 121.2 35.4 33.1 29.1Case 2 71.5 44.4 54.5 50.4 41.4 31.5 22.8Case 3 320.3 183.5 107.3 234.9 82.3 83.4 58.4
SSIM OF EACH IMAGE IN THE NUMERICAL SIMULATION STUDY.
Original BHC LI NMAR1 NMAR2 CNN CNN-MARCase 1 0.565 0.576 0.576 0.887 0.935 0.940 0.943Case 2 0.883 0.854 0.931 0.955 0.950 0.965 0.977Case 3 0.522 0.536 0.886 0.833 0.942 0.932 0.967
Image detection/classificationCheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar* 1
Jeremy Irvin* 1
Kaylie Zhu1 Brandon Yang
1 Hershel Mehta
1 Tony Duan
1 Daisy Ding
1 Aarti Bagul
1 Robyn L. Ball
2
arXiv:1711.05225v3 [cs.CV] 25 Dec 2017
Image detection/classification/regression
Original image with superimposed saliency map for sample hand radiographic
images in three male patients age 4 years (a), 15 years (b), and 17 years
Radiology: Volume 287: Number 1—April 2018 n radiology.rsna.org
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs1
Summary Statistics of Paired Interobserver Difference between Bone Age Estimate of Each Reviewer and Mean of the Other Three Human Reviewers’ Estimates, Compared
with That of Model
Variable Clinical Report Reviewer 1 Reviewer 2 Reviewer 3 Mean
Mean
Reviewer 0.08 -0.07 -0.07 0.06 0.00
Model 0.02 -0.01 -0.01 0.02 0.00
P value (paired t test) .41 .34 .36 .57
RMS
Reviewer 0.87 0.73 0.73 0.95 0.82
Model 0.65 0.67 0.67 0.68 0.67
P value (F test, comparing ratio of variances) < .01 .26 .23 < .01
MAD
Reviewer 0.65 0.55 0.53 0.69 0.61
Model 0.51 0.53 0.53 0.53 0.52
P value (paired t test) <.01 .50 .99 <.01
Deep learning and its application to medical image segmentationHolger R. ROTH1, Chen SHEN1, Hirohisa ODA2, Masahiro ODA1, Yuichi
HAYASHI1, Kazunari MISAWA3, Kensaku MORIarXiv:1803.08691v1 [cs.CV] 23 Mar 2018
TABLE : Quantitative results of the 3D FCN network in testing (n=37).Dice (%) Avg. Std. Min. Max.Artery 83.5% 4.1% 73.7% 91.1%vein 80.5% 6.8% 49.0% 89.4%liver 97.1% 1.0% 93.5% 98.3%spleen 97.7% 0.8% 95.2% 98.9%stomach 96.1% 7.9% 49.4% 98.9%Gallbladder 85.1% 15.7% 28.6% 97.4%Pancreas 84.9% 9.1% 52.5% 95.1%Total Avg. 89.3% 6.5% 63.1% 95.6%
Segmentation
The architecture of our 3D U-Net like fully convolutional network. It applies an end-to-end architecture using same size convolutions (via zero padding) with kernel sizes of 3 3 3.
Radiogenomics
CT synthesis from MRIDeep Embedding Convolutional Neural Network for Synthesizing CT Image
from T1-Weighted MR Image Lei Xiang1, Qian Wang1,*, Xiyao Jin1, Dong Nie3, Yu Qiao2, Dinggang Shen3,4,*
https://arxiv.org/pdf/1709.02073.pdf
Many more
• Tumor histology• Tumor grade• Prediction to RT response potential• Metastasis potential• Prediction to theraphy response• Prediction of survival• Prediction of motion and motion suppression• CT synthesis from MRI (MRI only RT)• Intracranial hemoraghy and infarct detection• Pulmonary thromboemboli detection• ….
Limitations:
• Training is data hungry
• How to train, in the case of practically unlimitednumber of targets (whole set of diagnosis, variation and artefact conditions)
• Each new scenoria requires new training, scalability to new conditions
Future pespective
• Scalibility
• Integration in different clinial environments
• Machine vs man--------->Machine+man vs man
• Imageomics……> Central position in precisionmedicine (Dr. Bradley Erickson Mayo Clinic)
Thank you for your attention…
https://mse238blog.stanford.edu/2017/08/imunizr/ai-takes-on-radiology/
Classification pipeline: Segmentation
• The extracted 3D tumor volume is saved in NRRD format in slicer and this is fed to ouralgorithm.
Classification pipeline: Feaure exrraction
Feature space, decision surface
Overfitting
Regularization:
*L2/L1 regularization*Drop out*Data augmentation*Early stopping
What is artificial intelligence
AI: Performing tasks of a machine
which are attributed to human
Machine learning: Performing tasks of a
machine without explicitly programming;
instead learning from the data fed to it
Artificial neural network:Computational modelsinspired by human neural cell
Deep learning: Computational models built bystacking multipl artfical neural network layers
Machine learning
• Supervised learning…Artifical neural networks
…Support vector machines (SVM)
…Decision trees
• Unsupervised learning…Self organising maps (SOM)
…k means clustering and otherclustering methods
http://slideplayer.com/slide/4380892/