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Big Pictures. Deep Diagnosis. - AIHelsinki · Big Pictures. Deep Diagnosis. Empowering Pathologists...
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Big Pictures. Deep Diagnosis. Empowering Pathologists with Deep Learning AI Tuomas Ropponen, CTO Fimmic Oy [email protected] +358 40 507 9974
Transcript of Big Pictures. Deep Diagnosis. - AIHelsinki · Big Pictures. Deep Diagnosis. Empowering Pathologists...
Big Pictures. Deep Diagnosis. Empowering Pathologists with Deep Learning AI
Tuomas Ropponen, CTOFimmic Oy
[email protected]+358 40 507 9974
Background
2013
Fimmic Oy founded
2014Start of operations
First customers:
2015-2017More customers, e.g.:
Pilot project in
Helsinki
Biobank
2002-2014
WebMicroscope in
academic projects
03/2018
Today
Launch of
Aiforia Cloud
& AI Training
Tools
06/2018
Launch of
Deep
Learning
Platform
03/2017
Launch to
Clinical sector
CE Mark (class I,
platform)
Fimmic Oy
A spin-off company from the Institute for Molecular Medicine
Finland (FIMM), University of Helsinki
Founded in 2013 by Medical Doctors and Life Science
Entrepreneurs
Operations in Helsinki, Finland & in Boston, US
22 employees; a great combination of expertise in medical field,
software development, artificial intelligence and machine vision
technologies, and life science business development.
Main fields of use: Medical Research, Drug Development, Medical
Education. Clinical Pathology in roadmap.
Customers in Europe, North America and Middle East
Digitalization
How to create a virtual/digital slide?
Images captured at high magnification
Up to 100 000 image tiles
Stitched digitally and compressed to a large picture
montage (Gb - Tb)
Samples
WorkflowDeep Learning AI-powered Image
Analysis
Pathologists ResearchersAny microscope
scanner
Educators Any device
Aiforia Tech stack and tech team
Production deployment in the Microsoft Azure and but can be installed also AWS or private cloud enviroments.
R&D deployments from laptop to other local hardware configurations
Neural network engine(modified open source(C++)), Backend(C#) and frontend(JavaScript/HTLM5).
Currently all R&D done in by experienced Finnish team in the Helsinki. Example. One of team members has coded the full neuralnetwork engine(Backpropagation etc…) 1st time from the scrats over 16 years ago for a 24/7 system…
My(Tuomas Ropponen, CTO) role it to lead the R&D. Over 30 products as tech lead/manager which 10 world best or 1st. Most of them is machine vision or 24/7 real-time systems.
Practical Deep learning experience in our domain
Not the amount of the labels but the quality (GIGO)
Typically 1% of the Raw data is needed to label.
In our Domain the Domain Expert critical resource and those does not have lot of free time…
No exact Ground truth. Current standard of Cancer diagnostic is Human estimation(Pathologist) from Physical glass slide NOT DIGITAL slide/image.
Digital Pathology
Easy sample archiving and retrieval
Fast sharing, remote consultation
Computer assisted analysis with Deep Learning AI
Challenges:
Gigapixel-sized files
Lack of standardization in image formats
Limited possibilities with conventional machine vision
Advanced Image Storage and
Collaboration tools in Cloud
Compatibility
Efficient compression
Deep Learning Algorithms &
Cloud computing
No local hardware needed
Endless possibilities for algorithms
On-demand & “Do-it-yourself”
Pay-per-use
Easy-to-deploy SaaS model
Low entrance fees
Solution - Aiforia® Cloud
Training of deep learning classifiers
1
Original Labelled
Whole slide samples
2Training set from sample regions
3 4Deep LearningApplication to new samples
Epithelium
Epithelium segmentation from breast cancer samples.
1. Laborious quantification, combined with ROI selection, e.g.
quantification of certain cells
2. Segmentation of tissue based on morphology, e.g. tumor
grading, epithelium/stroma segmentation
3. Detecting and quantifying rare targets, e.g. malaria infection
Deep Learning AI in Image Analysis
1. Epithelium-stroma segmentation
2. Quantification of Ki67 + and -cells inside the epithelium
Application example - Tedious quantification tasksBreast cancer diagnostics, Quantification of Ki67+ cells
Context-intelligent image analysis:
Enables full automationRemoves extra staining step
-> Saves time
AccurateConsistent
-> Supports correct diagnosis
Enables completely novel research approaches
Aiforia® Deep Learning Algorithms
Accurate, quantitative data
Consistent results, removes human error
Significant time savings - from hours to minutes
Cost savings through increased workflow efficiency
Fast, precise, more personalized care
The Future of Pathology is Digital
Supportive data for decision making ->
Prognosis ->
Suggesting treatment ->
Faster, more accurate diagnosis and cure
ContactTuomas Ropponen, CTO
+358 40 5079974
www.aiforia.com
