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

Email

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