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Image Analytics In Healthcare


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Machine learning has led to tremendous impact on healthcare - diagnosis and treatment. Employing image classification and image segmentation various diagnostics insights & solutions with automated report generation can be delivered in real-time, leading to faster and more informed decisions & streamlining costs.

Published in: Data & Analytics
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Image Analytics In Healthcare

  1. 1. Data Science for Healthcare: Image Analytics April 6, 2017
  2. 2. Page 2 © AlgoAnalytics All rights reserved Outline Image Analytics Work in Healthcare Machine Learning and Healthcare About AlgoAnalytics
  3. 3. Page 3 © AlgoAnalytics All rights reserved CEO and Company Profile Aniruddha Pant CEO and Founder of AlgoAnalytics PhD, Control systems, University of California at Berkeley, USA 2001 • 20+ years in application of advanced mathematical techniques to academic and enterprise problems. • Experience in application of machine learning to various business problems. • Experience in financial markets trading; Indian as well as global markets. Highlights • Experience in cross-domain application of basic scientific process. • Research in areas ranging from biology to financial markets to military applications. • Close collaboration with premier educational institutes in India, USA & Europe. • Active involvement in startup ecosystem in India. Expertise • Vice President, Capital Metrics and Risk Solutions • Head of Analytics Competency Center, Persistent Systems • Scientist and Group Leader, Tata Consultancy Services Prior Experience • Work at the intersection of mathematics and other domains • Harness data to provide insight and solutions to our clients Analytics Consultancy • +30 data scientists with experience in mathematics and engineering • Team strengths include ability to deal with structured/ unstructured data, classical ML as well as deep learning using cutting edge methodologies Led by Aniruddha Pant • Develop advanced mathematical models or solutions for a wide range of industries: • Financial services, Retail, economics, healthcare, BFSI, telecom, … Expertise in Mathematics and Computer Science • Work closely with domain experts – either from the clients side or our own – to effectively model the problem to be solved Working with Domain Specialists About AlgoAnalytics
  4. 4. AlgoAnalytics - One Stop AI Shop Healthcare •Medical Image diagnostics •Work flow optimization •Cash flow forecasting Financial Services •Dormancy prediction •Recommender system •RM risk analysis •News summarization Retail •Churn analysis •RecSys •Image recognition •Generating image description Others •Algorithmic trading strategies •Risk sensing – network theory •Network failure model •Clickstream analytics •News/ social media analytics Aniruddha Pant CEO and Founder of AlgoAnalytics • Structured data is utilized to design our predictive analytics solutions like churn, recommender sys • We use techniques like clustering, Recurrent Neural Networks, Structured data • We use text data analytics for designing solutions like sentiment analysis, news summarization and much more • We use techniques like Natural Language Processing (NLP), word2vec, deep learning, TF-IDF Text data • Image data is used for predicting existence of particular pathology, image recognition and many others • We employ techniques like deep learning – convolutional neural network (CNN), artificial neural networks (ANN) and technologies like TensorFlow Image data • We apply sound data to design factory solutions like air leakage detection, identification of empty and loaded strokes from press data, engine-compressor fault detection • We use techniques like deep learning Sound Data
  5. 5. Page 5 © AlgoAnalytics All rights reserved Technology:
  6. 6. Page 6 © AlgoAnalytics All rights reserved Healthcare: Areas of Analytics Application Patient Aid – Medical Diagnostics Revenue Cycle Management Resource Allocation Kind of proposals with higher chance of funding
  7. 7. Page 7 © AlgoAnalytics All rights reserved Machine Learning for Healthcare: Overview Medical diagnostics – Detecting serious disorders or diseases through image analytics and medico-genomic data Reducing Readmissions – using scoring techniques to locate high risk customers and preventing readmissions through higher monitoring Population Health Management - Using vast amounts of past patient data/ genomics data for diagnosing high risk group through risk stratification score Cash Flow Forecasting – Forecasting of cash flows based on claims history, reimbursement analysis and potential denials to forecast cash Billing Errors– Identify opportunities to collect missing income, including wrongfully rejected claims by payers or overdue money from patients. Patient Selection - Insurance coverage, Identifying patients especially those who are not likely to pay in full Work Flow Optimization– Using historical data for staffing to reduce costs, Having the right clinician at right time at right place Efficient Use of Hospital Resources – Prevent bottlenecks in urgent care by analyzing patient flow during peak times Fraud and abuse prevention - Cost trending-forecasting, care utilization analysis, actuarial and financial analysis are commonly used applications for preventing frauds Trend Analysis - Tracking hospital’s market position and competitive trends. And visualizing changes in market dynamics and growth patterns.. Grant problem - Predict likelihood that a particular proposal will receive grant using text analytics
  8. 8. Page 8 © AlgoAnalytics All rights reserved IMAGE ANALYTICS WORK IN HEALTHCARE
  9. 9. Page 9 © AlgoAnalytics All rights reserved AlgoAnalytics Image Analytics Expertise: Methods and Technologies Methods TechnologiesStatistical Learning Artificial Neural Network Convolutional Neural Network Transfer Learning (Deep Learning) TensorFlow-Python for neural networks (feed- forward and CNN) R Programming for statistical models: using pixel values as features and applying models such as logistic regression, random-forest Google’s Inception model (pre-trained TensorFlow model on Imagenet dataset) •In healthcare segment, classification algorithms help predict whether a particular pathology exists or not. •Image classification can be used in various solutions like •Diabetic ophthalmology •Brain MRI scan •Skin disease diagnosis Image Classification •Segmentation is the process of dividing image into regions with similar properties •Medical image segmentation aims at studying anatomical structures, locate tumour, lesions or other abnormaliaties •Some applications •Retinal vein segmentation, •Tumour segmentation •Aneurysm detection •Lung cancer, breast cancer, hepatocellular carcinoma Image segmentation •Gives description of what is occuring on the given set of images, with its caption, create a predictive model which generates relevant caption for the unseen images. •We use a combination of deep convolutional neural network (CNN) and LSTM language modeling (RNN) to generate captions •This can be used in automated report generation from image analytics Image description
  10. 10. Page 10 © AlgoAnalytics All rights reserved Diabetic Retinopathy Brain MRI Scan Image Classification - Healthcare
  11. 11. Page 11 © AlgoAnalytics All rights reserved Chest Xray Diagnostics - predict whether it is normal or nodular Input X-ray images VGGNet-16 Extracted featureset vectors Deep learning Featureset generation Logistic regression Classification Nodular (class -1) Vs. Normal(class -0) X-ray Results ● Classification Accuracy : 75% ● AUC : 81.29% ● Kappa Score : 47.3 % ● Deep Learning Techniques : ○ Maxpool-5 layer of a VGGNet-16 deep learning neural net to extract features from the X-rays. ○ Machine Learning algorithms (Logistic regtression, Random forest) to perform classification of the X-rays into 2 classes - 0 as Normal and 1 as abnormal. Dataset : The dataset consists of Images made available by the Department of Health and Human Services of Montgomery County, MD, USA. 138 posterior-anterior x-rays Out of which, 80 are normal X-rays and 58 x-rays are abnormal with manifestations of tuberculosis. The set covers a wide range of abnormalities Image Classification - Healthcare
  12. 12. Page 12 © AlgoAnalytics All rights reserved Image Segmentation - Automatic Brain Tumor Segmentation  Dataset : - All MRI data was provided by the 2015 MICCAI BraTS Challenge, which consists of approximately 250 high-grade glioma cases and 50 low-grade cases. - Each dataset contains four different MRI pulse sequences, each of which is comprised of 155 brain slices with each pixel representing a 1mm3 voxel, i.e., total of 620 images per patient. An example of a scan with the ground truth segmentation with the segmentation labels Original Image Normalized Image Final N4 Bias corrected Image Input preprocessing  Methodology : - Applied normalization and n4ITK bias correction on the original input images . - Generated 43264 patches per image for the input to the 4 layered Convolutional Neural Network (CNN) model. - Classification of tumor area based on 4 categories viz., edema, advancing tumor, non –advancing tumor and necrotic tumor core. Prediction Process using CNN model Results with segmented tumor
  13. 13. Page 13 © AlgoAnalytics All rights reserved Segmentation of blood vessel in retina Microaneurysms in color fundus image Image Segmentation Objective : Microaneurysms (MA) segmentation for early diabetic retinopathy screening using fundus images Dataset : Publicly available Retinopathy Online Challenge (ROC) dataset (total 50 fundus images) with pixel locations and radius of microaneurysms Deep Learning Approach : - Creating smaller patches of input images where center pixel is either MA or Non-MA - Predicting the probability of center pixel being MA using Convolutional Neural Network (CNN) model - Using trained CNN model to locate MAs in unseen fundus image
  14. 14. Page 14 © AlgoAnalytics All rights reserved Image Segmentation - Deep Learning for Ultrasound Nerve Segmentation Problem Statement: Accurately identifying nerve structures in ultrasound images for effectively inserting a patient’s pain management catheter. U-Net U-Net Trained Model Input Data Ultrasound Image Mask Test Image Predicted Mask Process: Step 1 - Train neural network on given ultrasound nerve images and their masks Step 2 - Use trained model to predict the mask for new test image Results on 500 test images: Dice Coefficient (pixelwise accuracy) = ~72%
  15. 15. Page 15 © AlgoAnalytics All rights reserved Benefits of Image Analytics in Healthcare Provides real-time insight to healthcare providers during diagnosis and treatment Improves the standard of care and speed of diagnosis, treatment and recovery Reduces varying subjective interpretation and human error Offers the potential to make faster and more informed decisions, streamline costs and broadly improve the quality and economics of healthcare Prediction of certain genetic disorders, monitor patients at risk Image Analytics in healthcare current estimated US market size is at $141 million and expected to grow at 8% CAGR The Benefits include:
  16. 16. Interested in knowing more? Contact us: April 6, 2017