Machine learning in healthcare and computer-assisted treatment

Miguel A. González Ballester

Larger players

Clinical diagnosis = Big Data

Bart Bijnens

Rocket platform

Rocket platformCollaborative platform

Heterogenous data and tools

Open source

Carlos Yagüe, Oscar Camara

UC1: VP2HF clinical managerClinical data of a patient

Data interpretation

Collaborative, multi-site

Easy configuration

e.g. automatic generation of decision trees

Carlos Yagüe, Oscar Camara

UC2: NEUBIAS platformApplication data

Image tools

Computation engine

Crowdsourcing of algorithms

Shared data and benchmarking

Open source

Carlos Yagüe, Chong Zhang

DL for image analysis

Real

MRI slice

12

8 ×

12

8

Synthetic

MRI slice

12

8 ×

12

8

12

8 ×

12

8 ×

n

12

8 ×

12

8 ×

n

64

×6

4 ×

2n

64

×6

4 ×

n32

×3

2

×3

n

32

×3

2

×n

16 × 16

× 4n

16 × 16

× n8 × 8 × 5n 8 × 8 × n

Embedding

Fu

lly

co

nn

ecte

d 8

×8

×5

n

Reshape

Fully connected 8 × 8 × n

3 × 3 × n

C1 C2 3 × 3 × 2n

C3 C4 3 × 3 × 3n

C5 C6

3 × 3 × 4n

C7 C8

3 × 3 × 5n

C9 C10

3 × 3 × n

C0 C1

3 × 3 × n

C2 C3

3 × 3 × n

C4 C5

3 × 3 × n

C6C7

3 × 3 × n

C8C9

3 × 3 × n

C0

3 × 3 × n

C10

Down-

sampling

2 × 2

Down-

sampling

2 × 2

Down-

sampling

2 × 2

Down-

sampling

2 × 2

Up-

sampling

2 × 2

Up-

sampling

2 × 2Up-

sampling

2 × 2Up-

sampling

2 × 2

ENCODER DECODER

Fetal imaging (Jordina Torrents)

Aortic aneurysms (Karen López-Linares)

Lung cancer (Xavi Rafael)

DL for image analysis

ML finds complex patterns

Eichstaedt JC, Psychological Science 2015

ML finds complex patterns

Foetal brain development

Age

Graph-Laplacian spectral image registration

Quantification of brain development

Ventriculomegaly

Veronika Zimmer, Gemma Piella

Foetal brain development

MANIFOLD LEARNING / MKL / NAFs…

• High dimensional dataset, contains dependencies and redundancies

• Data lies on manifold with intrinsic lower dimension

• Manifold learning: learn this lower dimensional representation

• High dimensional space of brain images, each brain represented as a point in

2D

Veronika Zimmer, Gemma Piella

Cardiac motion abnormalities

d = ???

Atlas of motion

Healthy subjects

Patient to study

Nicolas Duchateau, Gemma Piella

Cardiac motion abnormalities

Which statistics?

1. Population modelling (manifold learning)

2. Comparison of individuals to a population

3. Evolution with therapy

d = ???

Modelling pathological deviations from normality

(Medical Image Analysis, in

press)

Nicolas Duchateau, Gemma Piella

Computational modelling

ML & population models

Multiscale complex system

Outlook…

Models as “virtual twins”

Interpretable ML/DL

Uncertainty quantification

Implants & embedded intelligence

Synthetic biologyQAo

QpA

B

U

B

B

U

B

L

L

K K

LB LB

P

C

A

1

2

3

4

5

6

7 8

9

1

0

1

1

1

21

3

1

4

1

5

1

6

1

7

1

8

1

9

Bart Bijnens

Outlook…

Models as “virtual twins”

Interpretable ML/DL

Uncertainty quantification

Implants & embedded intelligence

Synthetic biology

Antoni Ivorra

Outlook…

Models as “virtual twins”

Interpretable ML/DL

Uncertainty quantification

Implants & embedded intelligence

Synthetic biology

Ricard Solé & Javier Macía, “Synthetic biology: Biocircuits in synchrony”

Nature 508, 326-327, 2014

x1

x2

xn

Input Hidden Output

y

Depth

Wid

th

Dendrite Terminal

Axon

Image: Quasar Jarosz

Algorithms

BCN_MedTech

ma.gonzalez@upf.edu

Roc Boronat 138, Barcelona, Spain

www.upf.edu/web/bcn-medtech