machine learning in healthcare and computer-assisted treatment · machine learning in healthcare...
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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
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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
Roc Boronat 138, Barcelona, Spain
www.upf.edu/web/bcn-medtech