Gael Varoquaux

Suresh Venkatasubramanian

Ion Stoica

Percy Liang

Dawn Song

2025-07-31

Science (publié)

Advancing Science- and Evidence-based AI Policy

Rishi Bommasani

Sanjeev Arora

Jennifer Chayes

Yejin Choi

Mariano-Florentino Cuéllar

Li Fei-Fei

Daniel E. Ho

Daniel Jurafsky

Sanmi Koyejo

Hima Lakkaraju

Arvind Narayanan

Alondra Nelson

Emma Pierson

Joelle Pineau

Scott Singer

Suresh Venkatasubramanian

Ion Stoica

Percy Liang

Dawn Song

Policy must be informed by, but also facilitate the generation of, scientific evidence.

2025-07-31

ArXiv (prépublication)

Advancing science- and evidence-based AI policy.

Rishi Bommasani

Sanjeev Arora

Jennifer Chayes

Yejin Choi

Mariano-Florentino Cuéllar

Li Fei-Fei

Daniel E. Ho

Dan Jurafsky

Sanmi Koyejo

Hima Lakkaraju

Arvind Narayanan

Alondra Nelson

Emma Pierson

Joelle Pineau

Scott R. Singer

Suresh Venkatasubramanian

Ion Stoica

Percy Liang

Dawn Song

Policy must be informed by, but also facilitate the generation of, scientific evidence.

2025-07-31

ArXiv (prépublication)

Individual Brain Charting dataset extension, third release for movie watching and retinotopy data

Ana Lúısa Pinho

Hugo Richard

Ana Fernanda Ponce

Michael Eickenberg

Alexis Amadon

Elvis Dohmatob

Isabelle Denghien

Juan Jesús Torre

Swetha Shankar

Himanshu Aggarwal

Alexis Thual

Thomas Chapalain

Chantal Ginisty

Séverine Becuwe-Desmidt

Séverine Roger

Yann Lecomte

Valérie Berland

Laurence Laurier

Véronique Joly-Testault

Gaëlle Médiouni-Cloarec … (voir 6 de plus)

Christine Doublé

Bernadette Martins

Stanislas Dehaene

Lucie Hertz-Pannier

Bertrand Thirion

2024-06-05

Scientific Data (publié)

Metrics reloaded: Pitfalls and recommendations for image analysis validation

Lena Maier-Hein

Annika Reinke

Evangelia Christodoulou

Ben Glocker

PATRICK GODAU

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

MICHAEL A. RIEGLER

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Minu Dietlinde Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (voir 47 de plus)

Spyridon Bakas

Peter Bankhead

Allison Benis

M. Jorge Cardoso

Veronika Cheplygina

BETH A. CIMINI

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

CHARLES E. KAHN

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

H. Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Felix Nickel

Brennan Nichyporuk

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clarisa S'anchez Guti'errez

SHRAVYA SHETTY

M. Smeden

Carole H. Sudre

Ronald M. Summers

Abdel Aziz Taha

Sotirios A. Tsaftaris

Ben Van Calster

PAUL F. JÄGER

2024-02-12

Nature Methods (publié)

Understanding metric-related pitfalls in image analysis validation

Annika Reinke

Minu Dietlinde Tizabi

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Carole H. Sudre

LAURA ACION

Michela Antonelli

Tal Arbel

Spyridon Bakas

Allison Benis

Arriel Benis

Matthew Blaschko

FLORIAN BUETTNER

M. Jorge Cardoso

Veronika Cheplygina

JIANXU CHEN

Evangelia Christodoulou … (voir 59 de plus)

BETH A. CIMINI

Gary S. Collins

Keyvan Farahani

LUCIANA FERRER

Adrian Galdran

Bram van Ginneken

Ben Glocker

PATRICK GODAU

Robert Cary Haase

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Fabian Isensee

Pierre Jannin

CHARLES E. KAHN

DAGMAR KAINMUELLER

BERNHARD KAINZ

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

H. Kenngott

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

BENNETT A. LANDMAN

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

M. Smeden

Ronald M. Summers

Abdel Aziz Taha

ALEKSEI TIULPIN

Sotirios A. Tsaftaris

Ben Van Calster

Manuel Wiesenfarth

ZIV R. YANIV

PAUL F. JÄGER

Lena Maier-Hein

2024-02-12

Nature methods (publié)

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Russell A. Poldrack

Christopher J. Markiewicz

Stefan Appelhoff

Yoni K. Ashar

Tibor Auer

Sylvain Baillet

Shashank Bansal

Leandro Beltrachini

Christian G. Benar

C. Bénar

Giacomo Bertazzoli

Suyash Bhogawar

Ross W. Blair

Marta Bortoletto

Mathieu Boudreau

Teon L. Brooks

Vince D. Calhoun

Filippo Maria Castelli

Patricia Clement

Alexander L. Cohen … (voir 100 de plus)

Julien Cohen-Adad

Sasha D’Ambrosio

Gilles de Hollander

María de la Iglesia-Vayá

Alejandro de la Vega

Arnaud Delorme

Orrin Devinsky

Dejan Draschkow

Eugene Paul Duff

E. Duff

Elizabeth DuPre

Eric Earl

Oscar Esteban

Franklin W. Feingold

Guillaume Flandin

Anthony Galassi

Giuseppe Gallitto

Melanie Ganz

Rémi Gau

James Gholam

Sulagna Dia Ghosh

Satrajit S. Ghosh

Alessio Giacomel

Ashley G. Gillman

Padraig Gleeson

Alexandre Gramfort

Samuel Guay

Giacomo Guidali

Yaroslav O. Halchenko

Daniel A. Handwerker

Nell Hardcastle

Peer Herholz

Dora Hermes

Christopher J. Honey

C. Honey

Robert B. Innis

Horea-Ioan Ioanas

Andrew Jahn

Agâh Karakuzu

David B. Keator

Gregory Kiar

Balint Kincses

Angela R. Laird

Jonathan C. Lau

Alberto Lazari

Jon Haitz Legarreta

Adam Li

Xiangrui Li

Bradley C. Love

Hanzhang Lu

Eleonora Marcantoni

Camille Maumet

Giacomo Mazzamuto

Steven L. Meisler

Mark Mikkelsen

Henk Mutsaerts

Thomas E. Nichols

Aki Nikolaidis

Gustav Nilsonne

Guiomar Niso

Martin Norgaard

Thomas W. Okell

Robert Oostenveld

Eduard Ort

Patrick J. Park

Mateusz Pawlik

Cyril R. Pernet

Franco Pestilli

Jan Petr

Christophe Phillips

Jean-Baptiste Poline

Luca Pollonini

Pradeep R. Raamana

Pradeep Reddy Raamana

Petra Ritter

Gaia Rizzo

Kay A. Robbins

Alexander P. Rockhill

Christine Rogers

Ariel Rokem

Chris Rorden

Alexandre Routier

Jose Manuel Saborit-Torres

Taylor Salo

Michael Schirner

Robert E. Smith

Tamas Spisak

Julia Sprenger

Nicole C. Swann

Martin Szinte

Sylvain Takerkart

Bertrand Thirion

Adam G. Thomas

Sajjad Torabian

Bradley Voytek

Julius Welzel

Martin Wilson

Tal Yarkoni

Krzysztof J. Gorgolewski

2024-01-09

ArXiv (prépublication)

Metrics Reloaded - A new recommendation framework for biomedical image analysis validation

Annika Reinke

Lena Maier-Hein

Evangelia Christodoulou

Ben Glocker

Patrick Scholz

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

Michael Alexander Riegler

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

Ali Emre Kavur

TIM RÄDSCH

Minu D. Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (voir 48 de plus)

Spyridon Bakas

Peter Bankhead

Arriel Benis

M. Jorge Cardoso

Veronika Cheplygina

Beth A Cimini

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Fred A Hamprecht

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

Charles Kahn

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

Hannes Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

Maarten van Smeden

Carole H. Sudre

Ronald M. Summers

Abdel A. Taha

Sotirios A. Tsaftaris

Ben Van Calster

Paul F Jaeger

Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There ar… (voir plus)e major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.

2022-05-09

MIDL.io/2022/Conference/Short (publié)

openreview.net

Population modeling with machine learning can enhance measures of mental health

Kamalaker Dadi

Josselin Houenou

Danilo Bzdok

Bertrand Thirion

Denis-Alexander Engemann

Background Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. Instead, individual differences in mental fun… (voir plus)ction are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. Conclusions Population modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations. Key Points We applied machine learning on more than 10.000 individuals from the general population to define empirical approximations of health-related psychological measures that do not require human judgment. We found that machine-learning enriched the given psychological measures via approximation from brain and sociodemographic data: Resulting proxy measures related as well or better to real-world health behavior than the original measures. Model comparisons showed that sociodemographic information contributed most to characterizing psychological traits beyond aging.

2021-09-24

bioRxiv (prépublication)

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Rémi Gau

Stephanie Noble

Katja Heuer

Katherine L. Bottenhorn

Isil P. Bilgin

Yu-Fang Yang

Julia M. Huntenburg

Johanna M.M. Bayer

Richard A.I. Bethlehem

Shawn A. Rhoads

Christoph Vogelbacher

V. Borghesani

Elizabeth Levitis

Hao-Ting Wang

Sofie Van Den Bossche

Xenia Kobeleva

Jon Haitz Legarreta

Samuel Guay

Selim Melvin Atay

Gael Varoquaux … (voir 199 de plus)

Dorien C. Huijser

Malin S. Sandström

Peer Herholz

Samuel A. Nastase

AmanPreet Badhwar

Guillaume Dumas

Simon Schwab

Stefano Moia

Michael Dayan

Yasmine Bassil

Paula P. Brooks

Matteo Mancini

James M. Shine

David O’Connor

Xihe Xie

Davide Poggiali

Patrick Friedrich

Anibal S. Heinsfeld

Lydia Riedl

Roberto Toro

César Caballero-Gaudes

Anders Eklund

Kelly G. Garner

Christopher R. Nolan

Damion V. Demeter

Fernando A. Barrios

Junaid S. Merchant

Elizabeth A. McDevitt

Robert Oostenveld

R. Cameron Craddock

Ariel Rokem

Andrew Doyle

Satrajit S. Ghosh

Aki Nikolaidis

Olivia W. Stanley

Eneko Uruñuela

Nasim Anousheh

Aurina Arnatkeviciute

Guillaume Auzias

Dipankar Bachar

Elise Bannier

Ruggero Basanisi

Arshitha Basavaraj

Marco Bedini

Lune Bellec

R. Austin Benn

Kathryn Berluti

Steffen Bollmann

Saskia Bollmann

Claire Bradley

Jesse Brown

Augusto Buchweitz

Patrick Callahan

Micaela Y. Chan

Bramsh Q. Chandio

Theresa Cheng

Sidhant Chopra

Ai Wern Chung

Thomas G. Close

Etienne Combrisson

Giorgia Cona

R. Todd Constable

Claire Cury

Kamalaker Dadi

Pablo F. Damasceno

Samir Das

Fabrizio De Vico Fallani

Krista DeStasio

Erin W. Dickie

Lena Dorfschmidt

Eugene P. Duff

Elizabeth DuPre

Sarah Dziura

Nathalia B. Esper

Oscar Esteban

Shreyas Fadnavis

Guillaume Flandin

Jessica E. Flannery

John Flournoy

Stephanie J. Forkel

Alexandre R. Franco

Saampras Ganesan

Siyuan Gao

José C. García Alanis

Eleftherios Garyfallidis

Tristan Glatard

Enrico Glerean

Javier Gonzalez-Castillo

Cassandra D. Gould van Praag

Abigail S. Greene

Geetika Gupta

Catherine Alice Hahn

Yaroslav O. Halchenko

Daniel Handwerker

Thomas S. Hartmann

Valérie Hayot-Sasson

Stephan Heunis

Felix Hoffstaedter

Daniela M. Hohmann

Corey Horien

Horea-Ioan Ioanas

Alexandru Iordan

Chao Jiang

Michael Joseph

Jason Kai

Agâh Karakuzu

David N. Kennedy

Anisha Keshavan

Ali R. Khan

Gregory Kiar

P. Christiaan Klink

Vincent Koppelmans

Serge Koudoro

Angela R. Laird

Georg Langs

Marissa Laws

Roxane Licandro

Sook-Lei Liew

Tomislav Lipic

Krisanne Litinas

Daniel J. Lurie

Désirée Lussier

Christopher R. Madan

Lea-Theresa Mais

Sina Mansour L

J.P. Manzano-Patron

Dimitra Maoutsa

Matheus Marcon

Daniel S. Margulies

Giorgio Marinato

Daniele Marinazzo

Christopher J. Markiewicz

Camille Maumet

Felipe Meneguzzi

David Meunier

Michael P. Milham

Kathryn L. Mills

Davide Momi

Clara A. Moreau

Aysha Motala

Iska Moxon-Emre

Thomas E. Nichols

Dylan M. Nielson

Gustav Nilsonne

Lisa Novello

Caroline O’Brien

Emily Olafson

Lindsay D. Oliver

John A. Onofrey

Edwina R. Orchard

Kendra Oudyk

Patrick J. Park

Mahboobeh Parsapoor

Lorenzo Pasquini

Scott Peltier

Cyril R. Pernet

Rudolph Pienaar

Pedro Pinheiro-Chagas

Jean-Baptiste Poline

Anqi Qiu

Tiago Quendera

Laura C. Rice

Joscelin Rocha-Hidalgo

Saige Rutherford

Mathias Scharinger

Dustin Scheinost

Deena Shariq

Thomas B. Shaw

Viviana Siless

Molly Simmonite

Nikoloz Sirmpilatze

Hayli Spence

Julia Sprenger

Andrija Stajduhar

Martin Szinte

Sylvain Takerkart

Angela Tam

Link Tejavibulya

Michel Thiebaut de Schotten

Ina Thome

Laura Tomaz da Silva

Nicolas Traut

Lucina Q. Uddin

Antonino Vallesi

John W. VanMeter

Nandita Vijayakumar

Matteo Visconti di Oleggio Castello

Jakub Vohryzek

Jakša Vukojević

Kirstie Jane Whitaker

Lucy Whitmore

Steve Wideman

Suzanne T. Witt

Hua Xie

Ting Xu

Chao-Gan Yan

Fang-Cheng Yeh

B.T. Thomas Yeo

Xi-Nian Zuo

2021-06-01

Neuron (publié)

Prediction, Not Association, Paves the Road to Precision Medicine

Danilo Bzdok

Ewout W. Steyerberg

2021-02-01

JAMA Psychiatry (publié)

Accounting for Variance in Machine Learning Benchmarks

Naz Sepah

Edward Raff

Kanika Madan

Vikram Voleti

Samira Ebrahimi Kahou

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (voir plus)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2021-01-01

MLSys (publié)