• No results found

Is the study of Machine Learning distracting for Neurosciences?

N/A
N/A
Info
Download
Protected

Academic year: 2021

Share "Is the study of Machine Learning distracting for Neurosciences?"

Copied!
42
0
0

Loading.... (view fulltext now)

Download now ( 42 Page )

Full text

(1)

Cliquez et modifiez le titre

en Arial 24 pt gras

Is the study of Machine Learning distracting for

Neurosciences?

(2)
(3)
(4)

Computational Anatomy

•

Quantification of brain structures

•

Quantification of brain lesions

(5)

And the Winner is …

•

ISLES 2017 challenge

•

Stroke : 14 participants, 14 NN ….

•

Trauma: 7 participants, 5 NN

•

Tumor: 22 participants, 19 NN, 1 SVM, 2 RF

•

ISLES 2018

•

Stroke : 5 finalists NN

•

Tumor: Segmentation 3 finalists NN, Survival 3 finalists NN

…

(6)

Life Science applications

•

Classification/segmentation/detection

Moeskops et al TMI 2016; Rajchl et al MIDL 2018 (brain tissue);

Dolz et al. Neuroimage 2018 (brain structures); Kamnitsas et al MediA 2016 (brain lesions) Kleesiek et al Neuroimage 2016 (brain extraction); Havaei et al MediA 2016 (brain tumors) Suk et al NeuroImage 2014 (AD/MCI)

Zhao et al MediA 2017; Suk et al NeuroImage 2016; Kin et al Neuroimage 2016 (functional brain networks)

Ciompi et al Scient Rep 2017 (lung nodules); Esteva et al Nature 2017 (skin cancer)

•

Synthetic image generation

Nie et al Miccai 2017 (MR-CT); Liu et al Radiology 2017 (MR-CT); Zhao et al Media 2018 (retinal images)

•

Predictive models

Polpin et al Nat Bio Eng 2018 (cardiovasular risk); Miotto et al. Scient Rep 2016 (deepPatient)

•

Processing

Rajchl et al MIDL 2018 (automatic process for segmentation), reconstruction, denoising, registration (see Litjens Media 2017)

•

Retrieval

Anavi et al Spie 2016

(7)

Open methodological questions

•

Influence of data processing

•

Data from different centres

•

Images augmentation

•

Strategy for selection of the best

architectures for a given domain application

(8)

Open methodological questions

•

Influence of data processing

•

Data from different centres

•

Images augmentation

•

Strategy for selection of the best

architectures for a given domain application

•

Use in clinical routine

Development of large databases

FLI-IAM web portal

Shanoir

Shanoir-SA

Predimed

Finger printing

Radiomics

Populse

MP3

(9)

ANN & Neurosciences – Cross fertilization

(10)

Mammalian visual system

[adapted from Cox & Dean curr bio 2014]

Simultaneous and parallel computing

J. Fabre 2012

(11)

Visual representation in the Human Brain

Bold

Eye-mvt

EEG

Model :

- Features

- Processing

(12)

Convolutional NN

(13)

V1-V2

V4

IT

FFA

PPA

[Dojat 2017]

ü

Receptive field

ü

Hierarchical model

ü

Max pooling

ü

Normalisation

CNN as a model of the visual system …

(14)

Hierarchical models for neural responses

prediction

(15)

Hierarchical models for neural responses

prediction

(16)

Hierarchical models for neural responses

prediction

[Yamins et al 2014 PNAS]

Representation dissimilarity matrices

(17)

Hierarchical models for neural responses

prediction

[Yamins et al 2014 PNAS]

(18)

Seen/imagined object arbitrary categories

[Horikawa and Kamitani Nat Comm 2018]

Train: 1200 im/50 obj cat (x24) Test: 50 im/50 obj cat (x35) 9 min each->9h15

1im/50 obj cat (x10) Idem test

10 min each ->3h33

CNN1…8 HMAX1..3

(19)

Decoding object arbitrary categories

[Horikawa and Kamitani Nat Comm 2018]

CNN1…8 HMAX1..3

GIST, SIFT+BoF

Train: 1200 jim/

150 ob

cat (x24) Test: 50 im/50 obj# cat (x35) 9 min each->9h15

1im/50 obj cat (x10) Test: 1im/50 obj# cat 10 min each ->3h33

(20)

Decoding object arbitrary categories

[Horikawa and Kamitani Nat Comm 2018]

CNN1…8 HMAX1..3

GIST, SIFT+BoF

Train: 1200 jim/

150 ob

cat (x24) Test: 50 im/50 obj# cat (x35) 9 min each->9h15

1im/50 obj cat (x10) Test: 1im/50 obj# cat 10 min each ->3h33

(21)

Decoding object arbitrary categories

[Horikawa and Kamitani Nat Comm 2018]

CNN1…8 HMAX1..3

GIST, SIFT+BoF

Train: 1200 jim/

150 ob

cat (x24) Test: 50 im/50 obj# cat (x35) 9 min each->9h15

1im/50 obj cat (x10) Test: 1im/50 obj# cat 10 min each ->3h33

(22)

Decoding object arbitrary categories

[Horikawa and Kamitani Nat Comm 2018]

CNN1…8 HMAX1..3

GIST, SIFT+BoF

Train: 1200 jim/

150 ob

cat (x24) Test: 50 im/50 obj# cat (x35) 9 min each->9h15

1im/50 obj cat (x10) Test: 1im/50 obj# cat 10 min each ->3h33

(23)

Synthesis

•

Isomorphism CNN layers – Visual areas up to IT)

•

Coherency of internal DNN & neural representation

(invasive & non invasive data, natural to movie stimuli, ventral

-dorsal)

•

Internal representation similarity

(imagined scenes)

•

Encoding

(stimulus -> brain representation)

(24)

[Lecun et al 2010]

ü

Receptive field

ü

Hierarchical model

ü

Max pooling

ü

Normalisation

ü

Isomorphism layers-areas

ü

Feedbacks

ü

Layer2layer conn.

ü

Nb layers / nb areas

ü

Eye mvt

ü

Magnitude factor

ü

Color

ü

Metamer

ü

Attention

ü

Local vs Global

ü

Perspective effects

ü

Learning

ü

Illusion - Hallucinations

ü

Adversarial example

ü

Sensor Fusion

(25)

[Lecun et al 2010]

ü

Receptive field

ü

Hierarchical model

ü

Max pooling

ü

Normalisation

ü

Isomorphism layers-areas

ü

Feedbacks

ü

Layer2layer conn.

ü

Nb layers / nb areas

ü

Eye mvt

ü

Magnitude factor

ü

Color

ü

Metamer

ü

Attention

ü

Local vs Global

ü

Perspective effects

ü

Learning

ü

Illusion - Hallucinations

CNN as a model of the visual system …

CNN as a tool to probe

specific situations

(26)

Water color effect

(27)

Results

I II III V1 0.45 0.5 0.55 0.6 0.65 0.7 0.75 Pre d ict io n Accu ra cy * * I II III V2 0.45 0.5 0.55 0.6 0.65 0.7 0.75 I II III V3v 0.45 0.5 0.55 0.6 0.65 0.7 0.75 Pre d ict io n Accu ra cy * * I II III hV4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 * * I II III LO 0.45 0.5 0.55 0.6 0.65 0.7 0.75 * * I II III V3d 0.45 0.5 0.55 0.6 0.65 0.7 0.75 Pre d ict io n Accu ra cy I II III V3A 0.45 0.5 0.55 0.6 0.65 0.7 0.75 * * I II III V7 0.45 0.5 0.55 0.6 0.65 0.7 0.75 I II III V3B/KO 0.45 0.5 0.55 0.6 0.65 0.7 0.75 * * I II III hMT+/V5 0.45 0.5 0.55 0.6 0.65 0.7 0.75 I: Edge-dependent vs Control II: Surface-dependent vs Control

III: Surface-dependent vs Edge-dependent

Retinotopic areas

Ventral visual areas

Dorsal visual areas

vs

vs

vs

ns ns ns ns ns ns ns
(28)

Conclusion

–

Filling-in is best classified and best correlate with appearance by

dorsal areas V3A & V3B/KO

–

Uniform chromaticity by ventral areas hV4 & LO

–

Feedback modulation from V3A to V1 and LO for filling-in

–

Feedback from LO modulating V1 and V3A for uniform chromaticity

LO V1 V3A LO V1 V3A

(29)
(30)

Hallucinations: recurrent patterns

Bressloff et al. (Phil Trans R Soc Lond B 2001)

In V1

(31)

Hallucinations

[Ffytche 2007 Dial Clin Neurosc]

PD

LSD

(32)

Hallucinations

[Ffytche 2007 Dial Clin Neurosc]

[Nguyen et al CVPR 2015]

PD

LSD

(33)

Hallucinations

(34)

Nobody is perfect …

blur

noise

(35)

Nobody is perfect …

[Dali 1941]

(36)

Biological plausibility

(37)

Predicting behavior of visual neurons

To what extent are predictive deep learning models of neural responses useful for

generating experimental hypotheses?

Receptive

field

Convolutional

filter

(38)
(39)

Predicting behavior of visual neurons

(40)

Consciousness model

Global

workspace

theory

[Baars 1988,

97, 2002]

Soar:

[Newell 1983, 92 ]

Global ignition

[Dehaene 2003]

(41)

CARMEN : Conscience Attention

Représentation Mentale

La Conscience dans tous ses

états

(42)

Casting

E. Barbier

T. Christen

M. Dojat

B. Lemasson

J. Warnking

C. Acquitter

F. Boux

L. Broche

C. Brossard

V. Kmetzsch

V. Munoz-Ramirez

S. Achard (Lig-Inria)

F. Forbes (Lig-Inria)

P. Coupé (Labri)

@GIN

@collab.

@miai

2 phd position

(see gin website)

References

Download now ( PDF - 42 Page - 45.41 MB )

Related documents

Constant time algorithms in sparse graph model

We construct a partitioning oracle that given query access to a graph from a specific family of graphs, provides query access to a fixed partition, and queries a

Application of system dynamics approach in electricity sector modelling: a review.

These research areas include models developed for policy assessment, generation capacity expansion, financial instruments, demand side management, mixing methods,

Householder Design Guide

When assessing a planning application for both single and two-storey extensions, two methods for applying the 45-degree rule will be used: Method 1: Considers the depth and width

Cardiac MRI endpoints in myocardial infarction experimental and clinical trials: JACC scientific expert panel

KEY WORDS area at risk, clinical trial, edema, endpoint, infarct size, magnetic resonance imaging, myocardial infarction, STEMI. APPENDIX For supplemental material, please see

QUALITY OF HUMAN RESOURCES: EDUCATION Vol. II - Graduate Level Education - Yu.D.Tretyakov

With the USA the most dominant economic power after World War II, it has not surprisingly been common in many countries in Europe to try to reshape their doctoral training so that

How To Become A Forensic Technician

Role: Involving the use of forensic applications to identify and recover evidence held on computer hard drives, mobile telephones and other digital media which have been

Extending a Web Browser with Client-Side Mining

WBext learns sophisticated user in- terests and browsing habits by tailoring and integrating data mining techniques including association rules mining, clustering, and text mining,

Overview of EPA Quality System Requirements

– Documentation of the organization's quality system (usually called a Quality Management Plan) which should be approved prior to initiating environmental work, and/or.