Combination of life science entrepreneurs, software development and machine vision experts & recognized scientists.

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2 Combination of life science entrepreneurs, software development and machine vision experts & recognized scientists. Kaisa Helminen CEO Previously: Sartorius Thermo Scientific Finnzymes Johan Lundin MD, CSO Co-Founder Board Member FIMM Karolinska Institute HUS Mikael Lundin MD, Director of Concept Design Co- Founder Board Member FIMM University of Helsinki Kari Pitkänen Business Tuomas Ropponen Development Co-Founder Board Member FIMM Fisher Scientific Finnzymes, cofounder, sold to Thermo Fisher Scientific in 2010 Outotec Biohit Delta-Enterprise CTO Mikael Jääskeläinen Sales Manager Sartorius Fisher Scientific Finnzymes Antti Merivirta Marketing Manager 360Visualizer Testure Finland

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4 Using Cloud-Based Deep Learning AI Platform to Analyze Gigantic Pathology Images Tuomas Ropponen, CTO Fimmic Oy October 28th, 2017

5 Every third person affected 14 Million new patients in % more by 2030 Increasing number of samples

6 Increasing number of samples Gap! Lack of pathologists

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8 Images captured at high magnification Up to image tiles Stitched digitally and compressed to a large picture montage (Gb - Tb)

9 Microscopy in tissues diagnostics Subjective analysis Manual visual methods Risk for variability in diagnosis

10 WebMicroscope Workflow Artificial Intelligence Samples Any microscope Pathologists Researchers Educators Any device scanner

11 Digitalization enables Easy sample archiving and retrieval Fast sharing, remote consultation Computer-assisted analysis with Deep Learning AI Challenges: Gigapixel-sized files Locked image file formats Limited analytics with conventional machine vision

12 Facial recognition Self-driving cars Tissue diagnostics id-labs.org The Guardian

13 1. Laborious quantification tasks, combined with region of interest selection, e.g. quantification of certain cells in epithelium 2. Segmentation of tissue based on morphology, e.g. tumor grading, epithelium/stroma segmentation 3. Detecting and quantifying rare targets, e.g. infectious agents, forensic samples

14 Training of deep learning classifiers 1 Whole slide samples 2 Training set from sample regions 3 Deep Learning 4 Application to new samples Epithelium Original Labelled Epithelium segmentation from breast cancer samples.

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21 FIMM Oxford collaboration 2017, unpublished results

22 FIMM Oxford collaboration 2017, unpublished results

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26 Significant time savings: From 45 minutes to 0,5 minutes analysis Unforeseen Accuracy & Reproducibility

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33 Breast cancer biomarkers: ER, PR, Ki67 + Epithelium/stroma segmentation Breast cancer, mitosis quantification Prostate cancer: Gland and epithelium segmentation Lung cancer, mouse tissue: Tumor burden, tumor classification Colon cancer, Ulcerative Cholitis Seminoma (testicular cancer): TIL% Liver biopsies: Hepatosteatosis, fibrosis Rat brain: Nerve cell bodies (Parkinsons, ALS research) Forensic pathology: sperm detection from smears Blood: RBC, WBC, Platelets, Malaria parasites etc.

34 WebMicroscope - Intelligent Image Analysis in Cloud Advanced Image Storage and Collaboration tools in Cloud Scanner vendor independent Deep Learning Algorithms & Cloud computing No local hardware needed Indefinite possibilities for algorithms Disruptive business model Affordable SaaS model for all sizes of projects

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36 Kaisa Helminen, CEO

37 FIMM Oxford collaboration 2017, unpublished results