20180126 microsoft ai on healthcare
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Microsoft provides various AI and machine learning tools and services. These include cognitive services, bots, Azure Machine Learning, and tools for building, training, deploying and managing AI models. Microsoft's portfolio addresses key questions around what engines to use, deployment targets, and whether to build your own models or consume pre-trained ones. The document also discusses trends in AI adoption like hybrid training/scoring scenarios and pushing inference to edge devices.
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20180126 microsoft ai on healthcare
- 1. Microsoft AI and cases sharing 蔡孟儒 Raymond Sr. Program Manager Customer Advisory Team, Azure C+E, GCR
- 3. Services & Tools Processing Frameworks AI Applications Cognitive Services Infrastructure AML Studio & Web Services BOT Framework Model & Experimentation Management Data Wrangling & Spark AI Batch Training Storage (Azure Data Services) & Hardware (CPU, GPU, FPGS & ASIC) Inferencing Spark, SQL, Other Engines DSVM Machine Learning and Deep Learning Toolkits CNTK Tensorflow ML Server Scikit-Learn Other Libs. ACS Docker Tooling CPUs Edge Dev DS
- 4. Machine Learning & AI Portfolio When to use what? What engine(s) do you want to use? Deployment target? Which experience do you want? Build your own or consume pre- trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server (On-prem) Hadoop SQL Server (cloud) AML Web Services SQL Server Spark Hadoop Azure Batch DSVM Azure Container Service (K8, Docker, DC/OS) Visual tooling (cloud) AML Studio Consume Cognitive services, bots
- 5. Tap into rich knowledge amassed from the web, academia, or your own data Access billions of web pages, images, videos, and news with the power of Bing APIs Process text and learn how to recognize what users want Hear and speak to your users by filtering noise, identifying speakers, and understanding intent Emerging Cognitive Services technologies for early adopters From faces to feelings, allow your apps to understand images and video
- 8. AI-powered Data Wrangling + E2E ML Dev Productivity + Deploy Anywhere = E2E Tooling for AI Development Program Synthesis Docker, Spark, IOT Edge, On prem, AWS/GCP… SPARK, GPU, Open Source Lifecycle Management
- 9. Data Prep with Program Synthesis Rapidly sample, understand, and prep data Leverage PROSE and more for intelligent data prep by example Extend/customize transforms and featurization through Python Generate Python and Pyspark for execution at scale
- 10. Built-in AI-powered Data Wrangling Collaboration with notebooks & Git Version control & reproducibility Metrics, lineage, run history, asset management, and more
- 11. Azure Container Service (scale out with Kubernetes clusters) Azure IoT Edge Spark on HDInsight On-prem, AWS, GCP….
- 13. Azure Data Science Virtual Machine • Popular tools Pre-installed & Pre-configured Includes RStudio & JuliaPro • Deep Learning Extension for Azure GPU VM • Developer Editions of SQL & R Server • Now available on Azure Batch • Supports Popular Workflows: SQL Server R Services: - Dev>Train>Test>Deploy>Score Using the Local Spark instance on the DSVM for Dev & Test Training and Deploying Deep Learning Models Using the ‘Deep Learning Toolkit for the DSVM’ on GPU based Azure VMs
- 14. Accelerating adoption of AI by developers (consuming models) Rise of hybrid training and scoring scenarios Push scoring/inference to the event (edge, cloud, on-prem) Some developers moving into deep learning as non-traditional path to DS / AI dev Growth of diverse hardware arms race across all form factors (CPU / GPU / FPGA / ASIC / device) Data prep Model deployment & management Model lineage & auditing Explain-ability A D O P T I N G A I : T R E N D S A N D C H A L L E N G E S C H A L L E N G E SK E Y T R E N D S
- 15. Services & Tools Processing Frameworks AI Applications Cognitive Services Infrastructure AML Studio & Web Services BOT Framework Model & Experimentation Management Data Wrangling & Spark AI Batch Training Storage (Azure Data Services) & Hardware (CPU, GPU, FPGS & ASIC) Inferencing Spark, SQL, Other Engines DSVM Machine Learning and Deep Learning Toolkits CNTK Tensorflow ML Server Scikit-Learn Other Libs. ACS Docker Tooling CPUs Edge Dev DS
- 17. Multiple Instance Learning MIL Training Cardinal Fish Classifier Weakly labeled training data Positive Instances Negative Instances (Maron 1997, Viola 2005)
- 18. Multiple Instance Learning MIL Training Cancer Image Classifier Weakly labeled training data Positive Instances Negative Instances
- 19. Parallel Multiple Instance Learning • A standard histopathology slice Resolution: 200,000 x 200,000 • Most existing medical imaging tools infeasible • Compute on multiple machines
- 20. Context-Constrained Multiple Instance Learning for Histopathology Image Segmentation
- 21. Using eye movement patterns for early detection of dyslexia in children Optolexia “The flexibility and ease of use of the Azure Machine Learning analytics platform makes it a perfect foundation for expanding our existing solution into new areas.“ Fredrik Wetterhall Chief Executive Officer at Optolexia The challenge Optolexia wanted the ability to iterate and scale their dyslexia detection model in order to accommodate expansion into schools, new environments, and enable additional condition screenings. Machine Learning in action • Visualized models, scoring, and results without writing new code to refine the testing tool • Screened over 1k students and identified signs of dyslexia earlier than ever before, leading to improved student care, education, and self-esteem • Created a scalable model enabling experimentation and testing with new languages and conditions Watch video
- 24. • Entity extraction is a subtask of information extraction (also known as Named-entity recognition (NER), entity chunking and entity identification). • Biomedical named entity recognition is a critical step for complex biomedical NLP tasks such as: • Extraction of diseases, symptoms from electronic medical or health records. • Understanding the interactions between different entity types such as drug-drug interaction, drug-disease relationship and gene-protein relationship, e.g., • Drug A cures Disease B. • Drug A causes Disease B. • Generic solution: Similar for other domains (e.g., legal, finance)
- 26. Machine Learning & AI Portfolio When to use what? What engine(s) do you want to use? Deployment target Which experience do you want? Build your own or consume pre- trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server (On-prem ) Hadoop SQL Server (cloud) AML Web Services SQL Server Spark Hadoop Azure Batch DSVM Azure Container Service (K8, Docker, DC/OS) Visual tooling (cloud) AML Studio Consume Cognitive services, bots
- 28. Appendix
- 29. Experimental Condition Datasets 30 non-cancer (NC) images and 53 colon cancer histopathology images Obtained from the Department of Pathology of Zhejiang University using Hamamatsu Nano Zoomer 2.0HT digital slice scanner Each image is independently labeled by two pathologists, the third pathologist moderates their discussion MTA—Moderately or well differentiated tubular adenocarcinoma PTA—Poorly differentiated tubular adenocarcinoma MA—Mucinous adenocarcinoma SRC—Signet-ring carcinoma
- 30. Results • Results: Pixel-level segmentation