Disrupting Cancer Diagnostics - Cloud-based Deep Learning AI for - - PowerPoint PPT Presentation

▶

Dec 05, 2023 26 likes •340 views

Disrupting Cancer Diagnostics - Cloud-based Deep Learning AI for Gigantic Pathology Images Kaisa Helminen, CEO Fimmic Oy May 11th, 2017 Cancer Every third person affected 14 Million new patients in 2012 +50% more by 2030 Increasing number

SLIDE 1

Disrupting Cancer Diagnostics - Cloud-based Deep Learning AI for Gigantic Pathology Images

Kaisa Helminen, CEO Fimmic Oy May 11th, 2017

SLIDE 2

Every third person affected 14 Million new patients in 2012 +50% more by 2030

Cancer

Increasing number of samples

SLIDE 3

Problem

Lack of pathologists Increasing number of samples

Gap!

SLIDE 4

Subjective, manual analysis

Microscopy in cancer diagnostics

Risk for misdiagnosis and wrong medication Agreement can be as low as 48%

SLIDE 5

Digitalization

Deep Learning AI

SLIDE 6

Gigapixel-sized files (Gb-Tb) Locked image file formats Image Analysis software are expensive

n-premise installations

Conventional machine vision technology is limited

Challenges

SLIDE 7

Samples

WebMicroscope Workflow

Artificial Intelligence

Pathologists Researchers Any microscope scanner Educators Any device

SLIDE 8

Artificial Intelligence & Deep Learning

Facial recognition

id-labs.org The Guardian

Self-driving cars Tissue diagnostics

SLIDE 9

Different types of Deep Learning Image Analysis tasks

1. Laborious quantification tasks, 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. infectious agents, forensic

samples

SLIDE 10

Application example 1 - Quantification task

Breast cancer diagnostics, Quantification of Ki67+ cells from epithelium

SLIDE 11

1. Epithelium-stroma

segmentation

2. Quantification of Ki67 + and -

cells inside the epithelium

SLIDE 12

Negative and weak signals Moderate and Strong

SLIDE 13

Context-intelligent Image Analysis

Enables full automation Removes extra staining step

> Saves time

Accurate Reproducible

> Supports correct diagnosis

SLIDE 14

SLIDE 15

Application example 2: Automated segmentation of cancer tissue TMA slides, prostate cancer

Tissue Microarray

SLIDE 16

Application example 2 - Automated segmentation task

Prostate cancer, Segmentation of cancer tissue, area quantification

SLIDE 17

Application example 3 - Quantification task

Testicular Cancer, Quantification of tumor infiltrating lymphocytes (TIL%)

H&E of immune cell rich region Heat map showing immune cell detec7on

FIMM – Oxford collabora0on 2017, unpublished results

SLIDE 18

Digitized whole slide images of testicular cancer are huge gigabyte-sized files Areas of infiltrating immune cells detected by automated analysis includes millions of immune cells (red areas)

Application example 3 - Quantification task

Testicular Cancer, Quantification of tumor infiltrating lymphocytes (TIL%)

FIMM – Oxford collabora0on 2017, unpublished results

SLIDE 19

Example patient: Immune cells in testicular cancer

Automated counting result Total immune cell count = 768.349 Immune cells/square mm tumor = 4223 Details of the analysis shown in the   video

FIMM – Oxford collabora0on 2017, unpublished results

SLIDE 20

Application example 4 - Quantification Task

Quantification of fat accumulation in liver cells

Consistent Accuracy and Reproducibility over large sample sets

SLIDE 21

SLIDE 22

Application example 5 - Quantification Task, complex background

Quantification of nerve cell bodies from rat brain tissue (Parkinson’s, Alzheimer’s) Significant time savings:

From 45 minutes to 0,5 minutes analysis

Unforeseen Accuracy & Reproducibility

SLIDE 23

Application example 6 - Identification of rare targets

Detection of Malaria infection in red blood cells

SLIDE 24

SLIDE 25

Examples of performed Deep Learning Image Analysis Applications

Breast cancer biomarkers: ER, Ki67, HER2 + Epithelium/stroma segmentation Prostate cancer: Gland and epithelium segmentation Lung cancer, mouse tissue: Tumor burden, tumor classification, TIL% Seminoma: TIL% Liver biopsies: Hepatosteatosis Rat brain: Nerve cell bodies (Parkinsons, ALS research) Forensic pathology: sperm detection from smears Blood: RBC, WBC, Platelets, Malaria parasites etc.

SLIDE 26

Browser-based Fluorescence Viewer

SLIDE 27

SLIDE 28

WebMicroscope - Intelligent Cloud Platform

Advanced Image Storage and Collaboration tools in Cloud

Compatibility Efficient compression

Deep Learning Algorithms & Cloud computing

No local hardware Indefinite possibilities for algorithms

Disruptive business model

Affordable SaaS model for all sizes of projects

SLIDE 29

The Future of Pathology is Digital

Supportive data for decision making -> Prognosis -> Suggesting treatment -> Faster, more accurate diagnosis and cure

SLIDE 30

Johan Lundin MD, CSO Co-Founder Board Member Mikael Lundin MD, Chief Data Scientist Co-Founder Board Member Kari Pitkänen Business Development Co-Founder Board Member Mikael Jääskeläinen Sales Manager Kaisa Helminen CEO Tuomas Ropponen CTO Sartorius Thermo Scientific Finnzymes Sartorius Fisher Scientific Finnzymes FIMM Fisher Scientific Finnzymes, co- founder, sold to Thermo Fisher Scientific in 2010 FIMM Karolinska Institute HUS FIMM University of Helsinki Outotec Biohit Delta-Enterprise Previously: Antti Merivirta Marketing Manager 360Visualizer Testure Finland

Experienced Core Team

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

SLIDE 31

Contact

Kaisa Helminen, CEO +358 40 679 0669 kaisa.helminen@fimmic.com www.webmicroscope.com @kaisa_helminen