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SMART IMAGING TECHNOLOGIES web-pathology.net
Machine Learning in Digital Pathology
Analyzing Pathology Slides with Machine Learning Methods
Machine Learning algorithms learn to recognize images and patterns in the same way humans do – by example, rather than by human-derived “handcrafted features” such as shape, size, brightness etc.
Adding Machine Learning methods to image analysis adds number of benefits:• No need to formalize complex “handcrafted features” by user,
pathologist can just point to patterns they need to recognize• No dependency on image analysis engineers (almost)• System can be trained on variety of samples to achieve robust
recognition• New data samples can be added to model easily to increase
accuracy
Since 2012, major improvement in visual recognition was achieved with so called deep learning neural networks. Latest generation of Visual Recognition Neural Networks achieve accuracy of recognition of natural objects similar to human observers. This area of technology is experiencing explosive growth.
Approach
Solutions
Her2 Scoring Nuclear Biomarkers: ER, PR, Ki-67
CD3 / CD8 Biomarker Scoring H&E Patterns (melanoma, IDC)
How It works
Turbo Upload
Analyze Stains
H&E Pattern Analysis:Melanoma, IDC, otherH&E Stain
Analyze Stain Type
Nuclear Biomarker Scoring:ER, PR, Ki-67
Her2 Scoring
Breast IHC Panel
CD3 / CD8 scoring, other specialty biomarkers
Nuclear Stain
Other
Membrane Stain
IHC Stain Generate Results
Visualize Results
Send notifications
HIPPA compliant email to user with results and link to the case
Smart Apps process whole slide automatically on upload and notify user when analysis is completed Analysis of a slide takes 2-8 minutes depending on application Analysis is seamlessly integrated with diagnostic workflow
Process Results
Extending Applications
Analysis applications can work on independent computing nodes running on local or remote servers Applications can use cloud-based recognition services via API Solutions can combine in-house algorithms with third party analysis routines Application location is transparent to end users, they use single web interface
Slide Store
Slide Server
Simagis Smart App
Simagis Digital Pathology Server
Local Algorithm
Algorithm 2
Third-party server
Algorithm 3
APIImaging Data
Application Server
Results
Application Server 2
Web Interface
Users
Location 2Location 1
Objective scoring of IHC biomarkers
Faster screening of tissue patterns, pattern search
Instant case reference. Pattern data mining
Predictive Analytics, Personalized Cancer Therapy, Integration with cancer knowledge bases
Suggestive Diagnosis, Expert Systems
Now
Future
Benefits of Image Analysis for Histopathology
Classified cancer pattern library is a valuable digital asset that can be licensed to other parties to train visual recognition and image analysis algorithms.
Visual recognition application can be used to automatically annotate digital pathology slides and link them with the rest of institutional cancer knowledge base. This application can be licensed to third parties to use for the same purposes.
Research and Clinical Applications:• Computer-assisted cancer diagnosis with pre-screening, suggestive
diagnosis options and contextual links to cancer knowledge libraries (similar cases, experts, research, additional tests etc.)
• Data mining and advanced analytics of historic tissue samples for cancer patients with known outcomes with the purpose of building predictive knowledge bases for cancer care and drug discovery.
Classified Pattern Library
Distributed Database
• Non SQL flexible indexed database architecture allows integrated storage of different data items across multiple locations
Comprehensive Data
• Flexible structure allows storing and integrating various data in the single information store• New data can be added to database structure at any time
Instant Search and Navigation
• Selection and navigation is possible for any data item in the database• Global search on any data is instant even for millions of items
Data Linking
• Data items can be linked with external data sources and knowledge bases such as diagnostic codes, SNOMED classifications or proprietary knowledge bases
We provide instant search, navigation and data mining ability across millions of slides
Integration
Integration: Information Systems
Easy API
• RESTful API with live examples and templates provide easy integration with third-party applications
LIS / EMR Systems
• Integration with other medical information systems is available via HL7 Integration Engine (Rhapsody by Orion Health)
Algorithms
• Third party image analysis application can access images and metadata
Knowledge Bases
• Information in the database can be integrated with other web based knowledge system via standard integration protocols
Our product includes standard industry data exchange protocols and APIs for integration with any third party application
Integration
Over 2000 registered product users on all continents
US Consultation Network of over 200 Pathologists with experts in every specialty
Clients and Partners
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