Highly Accurate Brain Stroke Diagnostic System and Generative Lesion Model. Junghwan Cho, Ph.D. CAIDE Systems, Inc. Deep Learning R&D Team

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1 Highly Accurate Brain Stroke Diagnostic System and Generative Lesion Model Junghwan Cho, Ph.D. CAIDE Systems, Inc. Deep Learning R&D Team

2 Established in September, 2016 at 110 Canal st. Lowell, MA 01852, USA CEO & Founder: Jacob Kyewook Lee Employees : 6 Contact: caideinfo@caidesystems.com Our Mission Save human lives by developing Cognitive Artificial Intelligence Disease Detection Systems. Provide protection of human life and equal access to health care and treatment through artificial intelligence technology. Our Goals Eliminating human errors and reducing delayed diagnosis Developing the most reliable AI system for analyzing images (ultra sound, MRI, CT and X-ray), electronic medical records, and genome data. Available Position Looking for Talented Research Scientist or Engineer With CAIDE, Better and Healthier Life!

3 Outlines CAIDE Diagnostic System for Brain Stroke Stroke Classification/Stroke Lesion Segmentation Stroke Lesion Generative Network Demo- CAIDE m: Studio BSR

4 Brain Stroke Brain attack/accident Up to 2 million brain cells die every minute. About 795,000 people suffer from stroke every year in US. More than 137,000 people (17% of all strokes) die from the stroke, with a cost of approximately $76.3 billion. Ischemic Stroke (Blood Blockage) Hemorrhagic Stroke (Bleeding) *Source image from With CAIDE, Better and Healthier Life!

5 CT Findings on Intracranial Hemorrhage Types Intraparenchymal (IPH) Intraventricular (IVH) Subarachnoid (SAH) Epidural (EDH) Subdural (SDH) IPH+IVH

6 END CAIDE Diagnostic System CT Images DICOM Files---> Gray Scale (on Window Level/Width) Preprocessing False Negative Model 1 CNN1- Classifier (Default Window) Cascaded CNN Classifiers VS Positive? (Bleeding) NO CNN2- Classifier (Stroke Window) Hemorrhagic(bleeding) No Bleeding YES Model 2 Stroke Lesion Delineation (FCN) YES Positive? NO

7 Sensitivity (Recall) vs Specificity True Positives Rate (Sensitivity) ROC for Classification ROC for Classification (0.953, 0983) (0.991, 0.961) Th p >=0.5 = (Specificity, Sensitivity) Th p >= False Positives Rate (1- Specificity) Sensitivity >> Specificity Cascaded CT window for increasing sensitivity while preserving specificity

8 Default Window vs Stroke Window Setting 50/100 (WL/WW) 40/40 50/100 Default Brain Window Stroke Window Ground Truth Narrow window width (high-contrast) Increase detection of subtle abnormalities Turner, P. J., and G. Holdsworth. "CT stroke window settings: an unfortunate misleading misnomer?." The British journal of radiology 84, no (2011):

9 Training for Cascaded CNN Classifier (Bleeding or not) Total data sets- 5,647 patients (3,000 no bleeding vs 2,647 bleeding) o2d axial CT images with 512x512 size 5-fold cross validation Trained cascaded CNN model otwo different training solvers: Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) oscratch vs fined tuned using pre-trained model Hardware computer: NVIDIA DGX-1 with 8 Tesla V100

10 Evaluation- Classification- (top 1- accuracy) after 15 epoch 100 % 98 ("#. "" ± &. '%) ("-. ". ± &..%) ("'. +' ± &. +%) 96 ("*. *+ ± &.,%) SGD- 1 SGD- 2 ADAM- 3 ADAM- 4 Scratch Fine-tuned Scratch Fine-tuned

11 Evaluation Cascade CT Window Increasing Sensitivity 100 % # of False Negative CT Images /100 (WL/WW) / /40 (Cascaded) Specificity Sensitivity 50/100 (WL/WW) 50/ /40 50/100 50/ /40 (Cascaded)

12 Outlines CAIDE Diagnostic System for Brain Stroke Stroke Lesion Segmentation Stroke Lesion Generative Network Demo- CAIDE m: Studio BSR

13 Encoder-Decoder Architecture - for sematic image segmentation SegNet, U-Net, and Fully Convolutional Network (FCN) Encoder Feature extraction (Convolution) Dimensional reduction (Pooling) VGG 16 or ResNet Decoder High resolution from low resolution Unpooling/up-sampling with transposed convolution (deconvolution) Source image from "Segnet, IEEE transactions on pattern analysis and machine intelligence 39, no. 12 (2017):

14 Fully Convolutional Network (FCN) for Stroke Lesion Segmentation VGG16 Network 2x upsampled SUM Pool4 prediction Pool3 prediction SUM 2x upsampled 8x upsampled Softmax FCN-8s Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

15 FCN Training Total data sets- 2,647 patients (corresponding 33,391 well labeled images) 5-fold cross validation Fully convolutional network with ADAM solver using pre-trained model NVIDA DGX-1 (8 V100 GPU) Histogram of Hemorrhagic Stroke Type IPH IVH EDH SDH SAH

16 Segmented Results by FCN8s after 50 Epoch Training IVH DC IVH =.86 IPH, SAH DC IPH =.89, DC SAH =.77

17 Segmented Results by FCN8s after 50 Epoch Training SDH: False Negative SAH, SDH DC SAH =. 84 SAH: False Positive EDH, SDH DC EDH =.89, DC SDH =.54

18 Performance Evaluation 100 Precision % % 100 #FP: Number of Pixels Falsely Positive Segmented DC: Dice Coefficient Recall, Sensitivity #FP>300 #FP>200 #FP> DC>5% DC>25% DC>50% Precision=TP/(TP+FP) Recall=TP/(TP+FN) IPH IVH EDH SDH SAH IPH IVH EDH SDH SAH

19 Outlines CAIDE Diagnostic System for Brain Stroke Stroke Classification/Stroke Lesion Segmentation Stroke Lesion Generative Network Demo- CAIDE m: Studio BSR

20 Generative Adversarial Networks (GANs) Two networks competing against each other in a zero sum game The discriminator (D): Distinguish real data from fake created by the generator (x) The generator (G): Learn distribution of the data from random noise, in an attempt to fool the discriminator Source Image from (z) Source Image from

21 Image to Image Translation - for Generating Stoke Lesion Images l l Apply to map stoke lesion labels to corresponding lesion image. Stoke lesion masks (segmented regions) - conditional input images to the Generator (G) as well as Discriminator (D) Lesion Generated Image Stoke Lesion Labels Source image from, Phillip, et al. "Image-to-image translation with conditional adversarial networks." arxiv preprint (2017) GAN for Generating Stoke Lesion Images Target (Stroke Lesion)

22 Training Pix2Pix-Tensorflow l Trained conditional GAN below conditions q Total data set : 2,647 patients (corresponding 33,391 well labeled images) : 80% training, 20% testing q Learning parameters: Learning rate =0.0002, L1 weight=100, and GAN weight=1.0 q About 16 hour up to 200 epoch on NVIDIA Tesla V100 (1 GPU)

23 Examples of Generated Fake CT Image after 200 Epoch Training Input Target Output Input Target Output SDH SAH EDH SAH SAH

24 Input Target Output SAH, EDH Input Target Output SAH, IVH, IPH IPH, SAH IVH, IPH SAH, SAHIVH SAH, IVH

25 Evaluation l l l l In general, evaluating GANs is difficult q q Loss function makes it harder during training FCN /Inception scores and Amazon Mechanical Turk (AMT by human) FCN scores : fake/generated images inferred by FCN Clarity- threshold blurriness (variance of Laplacian) Second discriminator- choose more realistic images IPH FCN scores (DICE) vs Image Quality Dice:.981 Dice:.976 Dice:.981 Dice: epochs 50 epochs 100 epochs 200 epochs

26 Evaluation Recall, DICE l Evaluated by varying: q Percentage of training data (based on patient number): 2.5, 10, 50, and 100% q Number of epochs : 10, 50, 100, and 200 epoch Recall DICE (FCN Scores)

27 Evaluation- Data Augmentation Original (Real Data) Original+ Augment Original+ 2xAugment Original+ 3xAugment

28 CAIDE m: Studio BSR (Demo), Booth #726