Xanadu Based Big Data Deep Learning for Medical Data Analysis

©2017 Xanadu Big Data, LLC All Rights Reserved www.xanadubigdata.com
Xanadu Based
Big Data Deep Learning
for Medical Data Analysis
November 9, 2017
Alex G. Lee (alexglee@xanadubigdata.com)

©2017 Xanadu Big Data, LLC All Rights Reserved
Index for Part I
-Deep Learning for Medical Data Analysis Introduction
-Automated Skin Cancer Classification
-Automated Diabetic Retinopathy Classification
-Brain Tumor Research
-Alzheime Prediction
-A Survey on Medical Image Deep Learning Research
-Cardiac Arrhthymia Detection
-ICU Patient Care

Index for Part II
-Deep Learning Introduction
-Convolution Process Details
-Issues with Big Data Deep Learning
-Distributed Deep Learning for Medical Big Data Analysis
-Challenges of Deep Learning for Medical Data Analysis
-Content Based Image Retrieval (CBIR)

Index for Part III
-Xanadu Functionality
-Xanadu Commodity Storage System Use Case
-Xanadu Cloud Computing Use Case
-Xanadu + Deep Learning + Hadoop + Spark Integration
-Xanadu based Big Data Deep Learning System for Medical Data
Analysis
-Xanadu CBIR Demo

Medical Image Classification and Segmentation:
Analysis of X-ray, CT, MRI, Ultrasound, PET, for Computer-Aided
Diagnosis in Radiology, Cardiology, and Pathology
Analysis of Microscopy Images for Disease Quantification
Content Based Medical Image Retrieval for Diagnostic Decision Making
Support
Medical Monitoring Signal Classification and Prediction:
Monitoring Signal Analysis for Automatic Health Risk Detection
Predictive Analysis for Clinical Decision Making Support
Deep Learning for Medical Data Analysis Introduction

Dermatologist-level classification of skin cancer with deep
neural networks by Stanford University researchers
Train Inception v3 CNN using a dataset of 129,450 clinical consisting of 2,032
different diseases: Test its performance against 21 board-certified
dermatologists on biopsy-proven clinical images with two critical binary
classification use cases: malignant carcinomas versus benign seborrheic
keratoses; and malignant melanomas versus benign nevi. The deep learning
CNN outperforms the average of the dermatologists at skin cancer
classification .
Automated Skin Cancer Classification
source: https://cs.stanford.edu/people/esteva/nature/

Google's Research In Artificial Intelligence Helps In
Preventing Blindness Caused by Diabetes
Google Brain has collaborated with doctors in India and US to help them
diagnose DR. The team has collected over 128,000 images that were each
evaluated by 3-7 ophthalmologists from a panel of 54 ophthalmologists. These
images were fed to a deep learning algorithm for creating a model to detect
Diabetic Retinopathy. The predictions from Google Brain’s neural network
algorithm were so close that it is considered to be on par with the
diagnosis of experienced ophthalmologists.
Automated Diabetic Retinopathy Classification
source: https://www.youtube.com/watch?v=oOeZ7IgEN4o

Deep Learning for Predicting Glioblastoma Subtypes from MRI
Deep learning can identify people likely to develop Alzheimer’s in the
next three years by studying positron emission tomography (PET)
scans of the brain.
Brain Tumor Research
source: https://www.youtube.com/watch?v=LVJbvK_HAbY
Alzheime Prediction
source: https://www.technologyreview.com/s/604289/software-predicts-cognitive-decline-using-
brain-images/

A Survey on Medical Image Deep Learning Research
source: https://arxiv.org/abs/1702.05747

Cardiologist-Level Arrhythmia Detection with Convolutional Neural
Networks
The electrocardiogram (ECG) is a tool to detect the electrical signal, which
could indicates malfunction of the heart. AF (Atrial Fibrillation) is the most
common sustained cardiac arrhythmia, occurring in 1-2% of the general
population and is associated with significant mortality and morbidity
through association of risk of death, stroke, hospitalization, heart failure
and coronary artery disease, etc.
Cardiac Arrhthymia Detection
source: https://physionet.org/challenge/2017/,
https://www.youtube.com/watch?time_continue=130&v=XVDDEsmbjuE

Deep Learning Helps Patient Care Decisions in Intensive Care Unit
Deep learning that takes large amounts of intensive-care-unit (ICU) data,
from vitals and labs to notes and demographics, can determine what kinds
of treatments are needed for different symptoms and who is at risk for
mortality by real-time predictions. Analysis of electroencephalographic
(EEG) recordings of brain activity for better treatment (e.g., epileptic
seizure).
ICU Patient Care
source: https://www.eecs.mit.edu/news-events/announcements/using-machine-learning-improve-
patient-care

Deep Learning Introduction
Convolutional Neural Networks (CNNs): image/video analysis
Source: Deep Learning A-Z™: Hands-On Artificial Neural Networks by SuperDataScience@udemy
Recurrent Neural Networks (RNNs):time series analysis
Reinforcement Learning (RL): automatically determine the ideal behaviour
within a specific context, in order to maximize its performance for a specific goal
Source: Reinforcement Learning in Python by Lazy Programmer Inc.@udemy

Convolution Process Details
input
1 filter
3x3 Kernel 15x15 Kernel
Activation
2x2 Pooling
Activation
+
Pooling
3 filters
Source: https://hackernoon.com/visualizing-parts-of-convolutional-neural-networks-using-keras-and-cats-
5cc01b214e59

Deep Learning Use Cases
CNN + RNN
for Real-time Object
Identification
Source: SoftPoint Consultores S.L.
Deep RL
for Self-driving Cars
Source: Mobileye

Issues with Big Data Deep Learning
Big Data Deep Learning
Volume:
Size of Data
Increase in algorithm complexity and
computing time
Velocity:
Data Processing Speed
Difficulty in data learning process in a
timely manner
Variety:
Different Types of Data
Increase in data processing diversity
for different data characteristics and
behavior
Veracity:
Truthfulness of Data
Significant increase in pre-processing
of data

Distributed Deep Learning for Medical Big Data Analysis
Issues with medical big data deep learning:
High resolution/scale microscopy images 81,025 pixels by 86,273 pixels
(roughly 6.99 gigapixels) requires 78.12 GB memory to store
Trained on 1.28 M images and evaluated on 50 K images took 2 – 3
weeks using 8 GPU machine in MS ResNet
Distributed training:
In model parallelism, different machines in the distributed system are
responsible for the computations in different parts of a single network - for
example, each layer in the neural network may be assigned to a different
machine.
In data parallelism, different machines have a complete copy of the model;
each machine simply gets a different portion of the data, and results from
each are somehow combined.
For details: Skymind web: http://engineering.skymind.io

Deep learning requires massive amount of training dataset as
accuracy of deep learning is largely dependent on the quality
and size of the dataset.
Development of large medical image and signal data is very
difficult as annotation requires extensive time from medical
experts.
Challenges of Deep Learning for Medical Data Analysis

CBIR is a way of searching image database using visual features of an
image as a search term to find images that share the similar visual features.
Content Based Image Retrieval (CBIR)
source: http://www.sciencedirect.com/science/article/pii/S0925231217308445

Xanadu enables competitive big data management in the Clouds or
Enterprises
Xanadu Functionality
K
AnyTypes&SizeofData
NoSQL Database
Data De-duplication
Data Replication
Massively Scalable Fault Tolerance
Data Store
NoSQL
Database
ACID Compliance
High
Throughput
Low Latency
Data
Access

Xanadu Performance BMT

Xanadu provides a composable architecture that can be integrated with
other big data systems
Xanadu Use Case
+
Total Integration
Big Data
Applications
Big Data
IT Infrastructure
Xanadu
Data Management
Platform
GPS /
GLONASS
WCDMA /
LTE
+

Xanadu Commodity Storage System Use Case

Xanadu Cloud Computing Use Case

CNN/RNN architecture in
DeepLearning4J (DL4J)
Distributed deep leaning on
CPUs/GPUs (+Hadoop/Spark)
Xanadu file system used to store
images and load directly into DL4J
Local Machines & AWS Clusters
Xanadu + Deep Learning + Spark + Hadoop Integration
Source: researchgate.net.
Data Source: https://www.kaggle.com/c/diabetic-retinopathy-detection

Xanadu Medical Data Big Data Archive
Xanadu’s deduplication reduces the actual storage footprint by recognizing
duplicate data and maintaining only as many copies as it needs to assure its
availability.
Time stamping functionality provides inherent (built-in) backup of the database.
ACID compliance guarantees reliable and consistent data update even in the face
of the storage failure.
Xanadu enables a fast content-based search system (e.g. CBIR) with massive
scalability
Xanadu provides database for real time big data deep learning
Xanadu provides intra/inter hospital collaborative data sharing platform.
Big Data Deep Learning integrated with Xanadu Medical Image Big
Data Archive
Customized deep learning for specific applications/dataset.
High speed deep learning over multiple CPU/GPU machines.
Xanadu based Big Data Deep Learning System
for Medical Data Analysis

Xanadu CBIR Demo

Thank you
Xanadu Big Data, LLC

Xanadu Based Big Data Deep Learning for Medical Data Analysis

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