Prof. Arvid Lundervold - VIS | TENK STORT. VIS DET. · •A guided tour of some mathematical and...

Nettverksmøte om medisinsk inkubator 8/3/2018Arvid Lundervold, UiB / MMIV

The role of AI in personalized therapy

Prof. Arvid Lundervold BSc, MD, PhD

Department of BiomedicineNeuroinformatics and Image Analysis Laboratory

University of Bergen&

Mohn Medical Imaging and Visualization CentreHaukeland University Hospital

with Assoc. prof. Alexander S. LundervoldDepartment of Computing, Mathematics and Physics, Western Norway University of Applied Sciences

Biomedical Network meeting on a Medical Innovation Incubator in Bergen, March 8th 2018

https://mmiv.no

17:45-17:55

W. Ertel, 2017

Bruk av AI til utvikling av persontilpasset terapi

https://bergento.eventsmart.com/events/nettverksmote-om-medisinsk-inkubator/

https://arvidl.github.io/pages/about.html

https://mmiv.no/

https://bergento.no/what-can-we-do-for-you/biomedical-network/

https://bergento.no/2018/02/make-medical-incubator-success

https://mmiv.no/


The role of AI in personalized therapy

Prof. Arvid Lundervold BSc, MD, PhD

Department of BiomedicineNeuroinformatics and Image Analysis Laboratory

University of Bergen&

Mohn Medical Imaging and Visualization CentreHaukeland University Hospital

with Assoc. prof. Alexander S. LundervoldDepartment of Computing, Mathematics and Physics, Western Norway University of Applied Sciences

Biomedical Network meeting on a Medical Innovation Incubator in Bergen, March 8th 2018

https://mmiv.no

17:45-17:55

W. Ertel, 2017



https://mmiv.no/

https://bergento.no/what-can-we-do-for-you/biomedical-network/

https://bergento.no/2018/02/make-medical-incubator-success

https://mmiv.no/


What is AI ?

Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., 2016https://developers.google.com/machine-learning/glossary

Machine Learning

• A program or system that builds (trains) a predictive model from input data.

• The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.

• Machine learning also refers to the field of study concerned with these

programs or systems.

“ The art of creating machines that per-form functions that require intelligencewhen performed by people”(Kurzweil, 1990)



https://mmiv.no/


https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017

Upcoming

- Neuromorphic hardware- Human augmentation- Brain-Computer Interface- Conversational UI- Edge computing / sensors- Smart robots- Virtual Assistants

Peak

- Deep learning- Machine learning

Maturation

- Cognitive Expert Advisors- Augmented Reality- Virtual Reality

MEDICINE

FEAR HYPE HOPE

InnovationTrigger

Peak ofInflated

Expectations

Through ofDisillusionment

Slope of Enlightenment Slope ofProductivity



https://mmiv.no/


MEDICINE AND THE “NEW”

COMPUTATIONAL FIELDS



https://mmiv.no/


Data Science `producing insights’ e.g. explorative and longitudinal data analysis

Artificial Intelligence `producing actions’ e.g. imaging-guided robot surgery

Machine Learning `producing predictions’ e.g. biomarkers treatment response

Computational science `producing governing equations’ e.g. tumor growth



https://mmiv.no/


Artificial Intelligencein medicine



https://mmiv.no/


Artificial Intelligencein medicine



https://mmiv.no/

Nettverksmøte om medisinsk inkubator 8/3/2018Arvid Lundervold, UiB / MMIVhttps://pct.mdanderson.org

Personalized therapy

in cancer



https://mmiv.no/

Nettverksmøte om medisinsk inkubator 8/3/2018Arvid Lundervold, UiB / MMIVhttps://pct.mdanderson.org

Personalized therapy

in cancer

… and then

Artificial intelligence-drivenbiopharmaceutical companies

e.g.http://www.twoxar.com

Virtualscreening

https://www.profacgen.com

Computer-AIded Drug Design

subject-specificas a service

on demand



https://mmiv.no/


MACHINE LEARNING

( an educational example )



https://mmiv.no/


Predicting academic achievement from inattention (SNAP)

A.J. Lundervold, T. Bøe, A. Lundervold. Inattention in primary school is not good for your future school achievement - a pattern classification study. PLoS ONE 2017;12(11): e0188310

Feature importanceRandom Forest

10000 trees (“weak learners”)

Top 3

k-fold cross validation

Prediction

Accuracy

Precision = tp / (tp + fp)Recall = tp / (tp + fn)



https://mmiv.no/


DEEP LEARNING



https://mmiv.no/


http://fortune.com/ai-artificial-intelligence-deep-machine-learning

Human vision vs. computer vision

http://neuro.cs.ut.ee/lab

Along the ventral stream the humanbrain represents increasingly morecomplex visual features. The verysame phenomenon emerges in deepartificial neural networks designed toclassify visual images: eachconsecutive layer of a deep neuralnetwork codes for more complexvisual features than the previous layer.



https://mmiv.no/


Non-invasive estimation of Glomerular Filtration Rate (GFR)

will need fast image segmentationof the kidney

Image-derived biomarkers

https://www.mayoclinic.org

Chronic kidney disease ↑Diabetes, hypertension, …

Functional renal imaging √



https://mmiv.no/


A. S. Lundervold, J. Rørvik, A. Lundervold

Fast semi-supervised segmentation of the

kidneys in DCE-MRI using convolutional

neural networks (CNN) and transfer learning

~ 50 hr

~ 5 hr

~ 5 sec

Alexander S. Lundervold et al.

Functional Renal Imaging: Where Physiology, Nephrology, Radiology and Physics Meet, Berlin 2017

(hippocampus)

Transfer learning

Manual3D labelling



https://mmiv.no/


• Promote cross-disciplinary research activities related to state-of-the-art imaging equipment (preclinical and clinical high field MRI, CT and hybrid PET/CT/MR)

• Aim: new methods in quantitative imaging and interactive visualization to predict changes in health and disease across spatial and temporal scales.

• Applications in basic research and preclinical validation

https://mmiv.no



https://mmiv.no/


The Mohn Medical Imaging and Visualization Centre

https://mmiv.no/machinelearning

Computational medical imaging and machine learning – methods, infrastructure and applications– A collaboration between the Department of Biomedicine, UiB, and the Department of Computing, Mathematics and Physics, HVL



https://mmiv.no/

http://www.uib.no/en/biomedisin

http://ict.hvl.no/


Radiology: Volume 278: Number 2—February 2016

Radiomics:

Computational imaging, machine learning, biomarkers, visual data science



https://mmiv.no/


Radiology: Volume 278: Number 2—February 2016

Computational imaging, machine learning, biomarkers, visual data science

Radiomics:

… but what if no image reconstruction

necessary

BMED360

?!

(measurements)



https://mmiv.no/


WE NEED EDUCATION& TRAINING

IN THE NEW FIELDS OF AI



https://mmiv.no/


Ultimately, machine learning in medicine will be a team sport, like medicine itself. But the team will need some new players: clinicians trained in statistics and computer science, who can contribute meaningfully to algorithm development and evaluation. Today’s medical education system is ill prepared to meet these needs.

… Undergraduate premedical requirements are absurdly outdated.

Medical education does little to train doctors in the data science,

statistics, or behavioral science required to develop, evaluate,

and apply algorithms in clinical practice.

Z. Obermeyer & T.H. Lee, Harvard Medical School

link



https://mmiv.no/

http://www.nejm.org/doi/full/10.1056/NEJMp1705348#t=article


A new elective course at the Faculty of Medicine (Spring 2019, 6 ETCS)

ELMED219

• The computational mindset, machine learning and AI in future medicine - pros et cons

• A guided tour of some mathematical and statistical modelling techniques in biomedical and clinical

applications. Examples and demonstrations will be related to in vivo imaging and integrated quantitative

physiology, imaging-derived biomarkers, omics data, and sensor data.

• Operational principles of selected sensors and measurement devices in biomedical research and clinical

practise - from smartphones to MRI scanners.

• The concepts of "big data", "data analytics", "machine learning", and "deep convolutional neural networks"

with examples from personalized and predictive medicine.

• Throughout the course, the students will use principles and tools from numerical programming, data

analysis, and scientific computing for medical applications. This will provide an introduction to e.g. R, Python,

and Jupyter notebooks, and "the cloud" for data storage and computations.

• The concepts and importance of "open science", "data sharing", and "reproducible research".



https://mmiv.no/

http://www.uib.no/en/course/ELMED219


Atom10-12 m

Protein10-9 m

Cell10-6 m

Tissue10-3 m

Organ100 m

Anatomy

Organ system& organism

Physiology

Gene-networks

Pathway models Stochastic models Ordinarydifferential-equations

Continuum models(Partial differential-

equations)

System-models

10-6 smolecular events

(ion channel gating)

10-3 sdiffusion

cell signaling

100 smotility

103 smitosis

106 sprotein

turnover

109 shumanlifetime

Brain

Spinal-cord

Peripher.nerves

TIME:

SPACE:

• -OMICS, IMAGING, PRECISION MEDICINE, DECISION-MAKING, PERSONALIZED MEDICINE and THERAPY

Fra: C. Dollery and R. Kitney, Systems biology: A vision for engineering and medicine, Tech. report, The Academy of Medical Sciences and The Royal Academy of Engineering, London, UK, Feb. 2007.

• INTERDISCIPLINARITY and COMPUTATIONAL APPROACHES to better understand, predict and control the

interplay between molecules, cells, tissue, and organs - in health and disease

AI will be incorporated in ….

D mindset

D skillset

D toolset

- open science

- reproducible research

Challenges:(mechanisms)

Computational medicinebody engineers ?



https://mmiv.no/


THANKS !



https://mmiv.no/


ML & Artificial Neural Networks (have been around)

Lundervold, A., Godtliebsen, F. Tissue classification in MR images using contextual and artificial neural network classifiers. In: Proceedings from the NOBIM conference.

15–16 June, 1992: 263–275.

Data Prediction

Learning / training

Training

database

Classifier

synaptic

weights

synaptic

weights

g – nonlinear activation function

PATTERN

RECOGNITION:

ANN



https://mmiv.no/


Mahjoubfar et al. Artificial Intelligence in Label-free Microscopy Biological Cell Classification by Time Stretch. Springer, 2017

http://nautil.us/issue/40/learning/is-artificial-intelligence-permanently-inscrutable

Explainable AI (XAI)

https://arxiv.org/abs/1712.09923

Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, Douglas B. Kell

What do we need to build explainable AI systems for

the medical domain?

The new European General Data Protection Regulation (GDPR and ISO/IEC 27001) entering into force on May 25th 2018, will make black-box approaches difficult to use in business …

https://www.darpa.mil/program/explainable-artificial-intelligence



https://mmiv.no/

https://arxiv.org/find/cs/1/au:+Holzinger_A/0/1/0/all/0/1

https://arxiv.org/find/cs/1/au:+Biemann_C/0/1/0/all/0/1

https://arxiv.org/find/cs/1/au:+Pattichis_C/0/1/0/all/0/1

https://arxiv.org/find/cs/1/au:+Kell_D/0/1/0/all/0/1


Hohman et al. Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers.https://arxiv.org/abs/1801.06889



https://mmiv.no/


UNDERSTANDING STROKE AND ALZHEIMER

Genetics

Epidemiology

Medical imagingOrgan-on-a-chip

Metabolomics

ECR Today, March 4, 2018

(the neurovascular unit)

• Imaging and -omics biomarkers• Early identification of persons at risk• Strategies for optimal prevention

http://www.costream.eu

Horizon 2020 #667375

Understanding and treating stroke and Alzheimer



https://mmiv.no/


Prostate cancer & machine learning

• Multi-parametric MRI (mpMRI)

• From digital histopathology to

Computational pathologyAre Losnegård et al.

Computerized Medical Imaging and Graphics 63 (2018) 24–30

Histology MRI

Feature selectionFeature selectionMachine learning

Multimodal image registration

Zhou et al. Large scale digital prostate pathology image analysis combining feature extraction and deep neural network. https://arxiv.org/abs/1705.02678



https://mmiv.no/






https://mmiv.no/




CART

Tree classification“ SNAP2 = 0 ? ”



https://mmiv.no/




CART

Feature importance

Random Forest10000 trees



https://mmiv.no/





10000 trees

Top 3

Prediction



https://mmiv.no/





10000 trees

Top 3

k-fold cross validation results

Accuracy = fraction of correct classifications

Precision = tp / (tp + fp)

Recall = tp / (tp + fn)



https://mmiv.no/

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