Brennan Nichyporuk

2025-03-27

MIDL.io/2025/Conference (published)

Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free

Gian Mario Favero

Parham Saremi

Emily Kaczmarek

Brennan Nichyporuk

Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of… (see more) complex data distributions. However, these models often need careful design, augmentation, and training techniques to ensure safe and reliable deployment. Recently, diffusion models have become synonymous with generative modeling in 2D. These models showcase robustness across a range of tasks including natural image classification, where classification is performed by comparing reconstruction errors across images generated for each possible conditioning input. This work presents the first exploration of the potential of class conditional diffusion models for 2D medical image classification. First, we develop a novel majority voting scheme shown to improve the performance of medical diffusion classifiers. Next, extensive experiments on the CheXpert and ISIC Melanoma skin cancer datasets demonstrate that foundation and trained-from-scratch diffusion models achieve competitive performance against SOTA discriminative classifiers without the need for explicit supervision. In addition, we show that diffusion classifiers are intrinsically explainable, and can be used to quantify the uncertainty of their predictions, increasing their trustworthiness and reliability in safety-critical, clinical contexts. Further information is available on our project page: https://faverogian.github.io/med-diffusion-classifier.github.io/

2025-02-06

ArXiv (preprint)

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

Daniel Duenias

Brennan Nichyporuk

Tammy Riklin-Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (see more)rds (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.

2024-03-20

ArXiv (preprint)

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Nima Fathi

Amar Kumar

Brennan Nichyporuk

Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (see more)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

2024-02-13

MIDL.io/2024/Conference/Submission (unknown)

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 … (see 47 more)

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

Gael Varoquaux

PAUL F. JÄGER

2024-02-12

Nature Methods (published)

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

Spyridon Bakas

Allison Benis

Arriel Benis

Matthew Blaschko

FLORIAN BUETTNER

M. Jorge Cardoso

Veronika Cheplygina

JIANXU CHEN

Evangelia Christodoulou … (see 59 more)

BETH A. CIMINI

Keyvan Farahani

LUCIANA FERRER

Gary S. Collins

Adrian Galdran

Bram van Ginneken

Ben Glocker

PATRICK GODAU

Daniel A. Hashimoto

Michael M. Hoffman

Robert Cary Haase

Merel Huisman

Fabian Isensee

Pierre Jannin

CHARLES E. KAHN

DAGMAR KAINMUELLER

BERNHARD KAINZ

ALEXANDROS KARARGYRIS

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Alan Karthikesalingam

H. Kenngott

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

Bennett A. Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Peter Mattson

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

Ronald M. Summers

Abdel Aziz Taha

ALEKSEI TIULPIN

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

M. Smeden

ZIV R. YANIV

PAUL F. JÄGER

Lena Maier-Hein

Manuel Wiesenfarth

2024-02-12

Nature methods (published)

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Nima Fathi

Amar Kumar

Brennan Nichyporuk

Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (see more)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

2024-01-01

MIDL (published)

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

Xing Shen

Hengguan Huang

Brennan Nichyporuk

2023-10-24

ArXiv (preprint)

Debiasing Counterfactuals in the Presence of Spurious Correlations

Amar Kumar

Nima Fathi

Raghav Mehta

Brennan Nichyporuk

Jean-Pierre R. Falet

Sotirios A. Tsaftaris

Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correl… (see more)ations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.

2023-10-09

Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (published)

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann

Annika Reinke

Vivienn Weru

Minu Dietlinde Tizabi

Fabian Isensee

T. Adler

PATRICK GODAU

Veronika Cheplygina

Michal Kozubek

Sharib Ali

Anubha Gupta

Jan. Kybic

Alison Professor Noble

Carlos Ortiz de Sol'orzano

Samiksha Pachade

Caroline Petitjean

Daniel Sage

Donglai Wei

Elizabeth Wilden

Deepak Alapatt … (see 334 more)

Vincent Andrearczyk

Ujjwal Baid

Spyridon Bakas

Niranjan Balu

Sophia Bano

Vivek Singh Bawa

Jorge Bernal

Sebastian Bodenstedt

Alessandro Casella

Jinwook Choi

Olivier Commowick

M. Daum

Adrien Depeursinge

Reuben Dorent

J. Egger

H. Eichhorn

Sandy Engelhardt

Melanie Ganz

Gabriel Girard

Lasse Donovan Hansen

Mattias Paul Heinrich

Nicholas Heller

Alessa Hering

Arnaud Huaulm'e

Hyunjeong Kim

Bennett Landman

Hongwei Bran Li

Jianning Li

Junfang Ma

Anne L. Martel

Carlos Mart'in-Isla

Bjoern Menze

Chinedu Innocent Nwoye

Valentin Oreiller

Nicolas Padoy

Sarthak Pati

Kelly Payette

Carole H. Sudre

K. V. Wijnen

Armine Vardazaryan

Tom Kamiel Magda Vercauteren

Martin Wagner

Chuanbo Wang

Moi Hoon Yap

Zeyun Yu

Chuner Yuan

Maximilian Zenk

Aneeq Zia

David Zimmerer

Rina Bao

Chanyeol Choi

Andrew Cohen

Oleh Dzyubachyk

Adrian Galdran

Tianyuan Gan

Tianqi Guo

Pradyumna Gupta

M. Haithami

Edward Ho

Ikbeom Jang

Zhili Li

Zheng Luo

Filip Lux

Sokratis Makrogiannis

Dominikus Muller

Young-Tack Oh

Subeen Pang

Constantin Pape

Görkem Polat

Charlotte Rosalie Reed

Kanghyun Ryu

Tim Scherr

Vajira L. Thambawita

Haoyu Wang

Xinliang Wang

Kele Xu

H.-I. Yeh

Doyeob Yeo

Yi Yuan

Yan Zeng

Xingwen Zhao

Julian Ronald Abbing

Jannes Adam

Nagesh Adluru

Niklas Agethen

S. Ahmed

Yasmina Al Khalil

Mireia Alenya

Esa J. Alhoniemi

C. An

Talha E Anwar

Tewodros Arega

Netanell Avisdris

D. Aydogan

Yi-Shi Bai

Maria Baldeon Calisto

Berke Doga Basaran

Marcel Beetz

Cheng Bian

Hao-xuan Bian

Kevin Blansit

Louise Bloch

Robert Bohnsack

Sara Bosticardo

J. Breen

Mikael Brudfors

Raphael Brungel

Mariano Cabezas

Alberto Cacciola

Zhiwei Chen

Yucong Chen

Dan Chen

Minjeong Cho

Min-Kook Choi

Chuantao Xie Chuantao Xie

Dana Cobzas

Julien Cohen-Adad

Jorge Corral Acero

Sujit Kumar Das

Marcela de Oliveira

Hanqiu Deng

Guiming Dong

Lars Doorenbos

Cory Efird

Di Fan

Mehdi Fatan Serj

Alexandre Fenneteau

Lucas Fidon

Patryk Filipiak

Ren'e Finzel

Nuno Renato Freitas

C. Friedrich

Mitchell J. Fulton

Finn Gaida

Francesco Galati

Christoforos Galazis

Changna Gan

Zheyao Gao

Sheng Gao

Matej Gazda

Beerend G. A. Gerats

Neil Getty

Adam Gibicar

Ryan J. Gifford

Sajan Gohil

Maria Grammatikopoulou

Daniel Grzech

Orhun Guley

Timo Gunnemann

Chun-Hai Guo

Sylvain Guy

Heonjin Ha

Luyi Han

Ilseok Han

Ali Hatamizadeh

Tianhai He

Ji-Wu Heo

Sebastian Hitziger

SeulGi Hong

Seungbum Hong

Rian Huang

Zi-You Huang

Markus Huellebrand

Stephan Huschauer

M. Hussain

Tomoo Inubushi

Ece Isik Polat

Mojtaba Jafaritadi

Seonghun Jeong

Bailiang Jian

Yu Jiang

Zhifan Jiang

Yu Jin

Smriti Joshi

A. Kadkhodamohammadi

R. A. Kamraoui

Inhak Kang

Jun-Su Kang

Davood Karimi

April Ellahe Khademi

Muhammad Irfan Khan

Suleiman A. Khan

Rishab Khantwal

Kwang-Ju Kim

Timothy Lee Kline

Satoshi Kondo

Elina Kontio

Adrian Krenzer

Artem Kroviakov

Hugo J. Kuijf

Satyadwyoom Kumar

Francesco La Rosa

Abhishek Lad

Doohee Lee

Minho Lee

Chiara Lena

Hao Li

Ling Li

Xingyu Li

F. Liao

Kuan-Ya Liao

Arlindo L. Oliveira

Chaonan Lin

Shanhai Lin

Akis Linardos

M. Linguraru

Han Liu

Tao Liu

Dian Liu

Yanling Liu

Joao Lourencco-Silva

Jing Lu

Jia Lu

Imanol Luengo

Christina Bach Lund

Huan Minh Luu

Yingqi Lv

Uzay Macar

Leon Maechler

L. SinaMansour

Kenji Marshall

Moona Mazher

Richard McKinley

Alfonso Medela

Felix Meissen

Mingyuan Meng

Dylan Bradley Miller

S. Mirjahanmardi

Arnab Kumar Mishra

Samir Mitha

Hassan Mohy-ud-Din

Tony C. W. Mok

Gowtham Krishnan Murugesan

Enamundram Naga Karthik

Sahil Nalawade

Jakub Nalepa

M. Naser

Ramin Nateghi

Hammad Naveed

Quang-Minh Nguyen

Cuong Nguyen Quoc

Brennan Nichyporuk

Bruno Oliveira

David Owen

Jimut Bahan Pal

Junwen Pan

W. Pan

Winnie Pang

Bogyu Park

Vivek G. Pawar

K. Pawar

Michael Peven

Lena Philipp

Tomasz Pieciak

Szymon S Płotka

Marcel Plutat

Fattane Pourakpour

Domen Prelovznik

K. Punithakumar

Abdul Qayyum

Sandro Queir'os

Arman Rahmim

Salar Razavi

Jintao Ren

Mina Rezaei

Jonathan Adam Rico

ZunHyan Rieu

Markus Rink

Johannes Roth

Yusely Ruiz-gonzalez

Numan Saeed

Anindo Saha

Mostafa M. Sami Salem

Ricardo Sanchez-matilla

Kurt G Schilling

Weizhen Shao

Zhiqiang Shen

Ruize Shi

Pengcheng Shi

Daniel Sobotka

Th'eodore Soulier

Bella Specktor Fadida

D. Stoyanov

Timothy Sum Hon Mun

Xiao-Fu Sun

Rong Tao

Franz Thaler

Antoine Th'eberge

Felix Thielke

Helena R. Torres

K. Wahid

Jiacheng Wang

Yifei Wang

W. Wang

Xiong Jun Wang

Jianhui Wen

Ning Wen

Marek Wodziński

Yehong Wu

Fangfang Xia

Tianqi Xiang

Cheng Xiaofei

Lizhang Xu

Tingting Xue

Yu‐Xia Yang

Lingxian Yang

Kai Yao

Huifeng Yao

Amirsaeed Yazdani

Michael Yip

Hwa-Seong Yoo

Fereshteh Yousefirizi

Shu-Fen Yu

Lei Yu

Jonathan Zamora

Ramy Ashraf Zeineldin

Dewen Zeng

Jianpeng Zhang

Bokai Zhang

Jiapeng Zhang

Fangxi Zhang

Huahong Zhang

Zhongchen Zhao

Zixuan Zhao

Jia Zhao

Can Zhao

Q. Zheng

Yuheng Zhi

Ziqi Zhou

Baosheng Zou

Klaus Maier-Hein

PAUL F. JÄGER

Annette Kopp-Schneider

Lena Maier-Hein

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practic… (see more)e. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

2022-12-16

ArXiv (preprint)

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann

Annika Reinke

Vivienn Weru

Minu Dietlinde Tizabi

Fabian Isensee

T. Adler

PATRICK GODAU

Veronika Cheplygina

Michal Kozubek

Sharib Ali

Anubha Gupta

Jan. Kybic

Alison Professor Noble

Carlos Ortiz de Sol'orzano

Samiksha Pachade

Caroline Petitjean

Daniel Sage

Donglai Wei

Elizabeth Wilden

Deepak Alapatt … (see 334 more)

Vincent Andrearczyk

Ujjwal Baid

Spyridon Bakas

Niranjan Balu

Sophia Bano

Vivek Singh Bawa

Jorge Bernal

Sebastian Bodenstedt

Alessandro Casella

Jinwook Choi

Olivier Commowick

M. Daum

Adrien Depeursinge

Reuben Dorent

J. Egger

H. Eichhorn

Sandy Engelhardt

Melanie Ganz

Gabriel Girard

Lasse Donovan Hansen

Mattias Paul Heinrich

Nicholas Heller

Alessa Hering

Arnaud Huaulm'e

Hyunjeong Kim

Bennett Landman

Hongwei Bran Li

Jianning Li

Junfang Ma

Anne L. Martel

Carlos Mart'in-Isla

Bjoern Menze

Chinedu Innocent Nwoye

Valentin Oreiller

Nicolas Padoy

Sarthak Pati

Kelly Payette

Carole H. Sudre

K. V. Wijnen

Armine Vardazaryan

Tom Kamiel Magda Vercauteren

Martin Wagner

Chuanbo Wang

Moi Hoon Yap

Zeyun Yu

Chuner Yuan

Maximilian Zenk

Aneeq Zia

David Zimmerer

Rina Bao

Chanyeol Choi

Andrew Cohen

Oleh Dzyubachyk

Adrian Galdran

Tianyuan Gan

Tianqi Guo

Pradyumna Gupta

M. Haithami

Edward Ho

Ikbeom Jang

Zhili Li

Zheng Luo

Filip Lux

Sokratis Makrogiannis

Dominikus Muller

Young-Tack Oh

Subeen Pang

Constantin Pape

Görkem Polat

Charlotte Rosalie Reed

Kanghyun Ryu

Tim Scherr

Vajira L. Thambawita

Haoyu Wang

Xinliang Wang

Kele Xu

H.-I. Yeh

Doyeob Yeo

Yi Yuan

Yan Zeng

Xingwen Zhao

Julian Ronald Abbing

Jannes Adam

Nagesh Adluru

Niklas Agethen

S. Ahmed

Yasmina Al Khalil

Mireia Alenya

Esa J. Alhoniemi

C. An

Talha E Anwar

Tewodros Arega

Netanell Avisdris

D. Aydogan

Yi-Shi Bai

Maria Baldeon Calisto

Berke Doga Basaran

Marcel Beetz

Cheng Bian

Hao-xuan Bian

Kevin Blansit

Louise Bloch

Robert Bohnsack

Sara Bosticardo

J. Breen

Mikael Brudfors

Raphael Brungel

Mariano Cabezas

Alberto Cacciola

Zhiwei Chen

Yucong Chen

Dan Chen

Minjeong Cho

Min-Kook Choi

Chuantao Xie Chuantao Xie

Dana Cobzas

Julien Cohen-Adad

Jorge Corral Acero

Sujit Kumar Das

Marcela de Oliveira

Hanqiu Deng

Guiming Dong

Lars Doorenbos

Cory Efird

Di Fan

Mehdi Fatan Serj

Alexandre Fenneteau

Lucas Fidon

Patryk Filipiak

Ren'e Finzel

Nuno Renato Freitas

C. Friedrich

Mitchell J. Fulton

Finn Gaida

Francesco Galati

Christoforos Galazis

Changna Gan

Zheyao Gao

Sheng Gao

Matej Gazda

Beerend G. A. Gerats

Neil Getty

Adam Gibicar

Ryan J. Gifford

Sajan Gohil

Maria Grammatikopoulou

Daniel Grzech

Orhun Guley

Timo Gunnemann

Chun-Hai Guo

Sylvain Guy

Heonjin Ha

Luyi Han

Ilseok Han

Ali Hatamizadeh

Tianhai He

Ji-Wu Heo

Sebastian Hitziger

SeulGi Hong

Seungbum Hong

Rian Huang

Zi-You Huang

Markus Huellebrand

Stephan Huschauer

M. Hussain

Tomoo Inubushi

Ece Isik Polat

Mojtaba Jafaritadi

Seonghun Jeong

Bailiang Jian

Yu Jiang

Zhifan Jiang

Yu Jin

Smriti Joshi

A. Kadkhodamohammadi

R. A. Kamraoui

Inhak Kang

Jun-Su Kang

Davood Karimi

April Ellahe Khademi

Muhammad Irfan Khan

Suleiman A. Khan

Rishab Khantwal

Kwang-Ju Kim

Timothy Lee Kline

Satoshi Kondo

Elina Kontio

Adrian Krenzer

Artem Kroviakov

Hugo J. Kuijf

Satyadwyoom Kumar

Francesco La Rosa

Abhishek Lad

Doohee Lee

Minho Lee

Chiara Lena

Hao Li

Ling Li

Xingyu Li

F. Liao

Kuan-Ya Liao

Arlindo L. Oliveira

Chaonan Lin

Shanhai Lin

Akis Linardos

M. Linguraru

Han Liu

Tao Liu

Dian Liu

Yanling Liu

Joao Lourencco-Silva

Jing Lu

Jia Lu

Imanol Luengo

Christina Bach Lund

Huan Minh Luu

Yingqi Lv

Uzay Macar

Leon Maechler

L. SinaMansour

Kenji Marshall

Moona Mazher

Richard McKinley

Alfonso Medela

Felix Meissen

Mingyuan Meng

Dylan Bradley Miller

S. Mirjahanmardi

Arnab Kumar Mishra

Samir Mitha

Hassan Mohy-ud-Din

Tony C. W. Mok

Gowtham Krishnan Murugesan

Enamundram Naga Karthik

Sahil Nalawade

Jakub Nalepa

M. Naser

Ramin Nateghi

Hammad Naveed

Quang-Minh Nguyen

Cuong Nguyen Quoc

Brennan Nichyporuk

Bruno Oliveira

David Owen

Jimut Bahan Pal

Junwen Pan

W. Pan

Winnie Pang

Bogyu Park

Vivek G. Pawar

K. Pawar

Michael Peven

Lena Philipp

Tomasz Pieciak

Szymon S Płotka

Marcel Plutat

Fattane Pourakpour

Domen Prelovznik

K. Punithakumar

Abdul Qayyum

Sandro Queir'os

Arman Rahmim

Salar Razavi

Jintao Ren

Mina Rezaei

Jonathan Adam Rico

ZunHyan Rieu

Markus Rink

Johannes Roth

Yusely Ruiz-gonzalez

Numan Saeed

Anindo Saha

Mostafa M. Sami Salem

Ricardo Sanchez-matilla

Kurt G Schilling

Weizhen Shao

Zhiqiang Shen

Ruize Shi

Pengcheng Shi

Daniel Sobotka

Th'eodore Soulier

Bella Specktor Fadida

D. Stoyanov

Timothy Sum Hon Mun

Xiao-Fu Sun

Rong Tao

Franz Thaler

Antoine Th'eberge

Felix Thielke

Helena R. Torres

K. Wahid

Jiacheng Wang

Yifei Wang

W. Wang

Xiong Jun Wang

Jianhui Wen

Ning Wen

Marek Wodziński

Yehong Wu

Fangfang Xia

Tianqi Xiang

Cheng Xiaofei

Lizhang Xu

Tingting Xue

Yu‐Xia Yang

Lingxian Yang

Kai Yao

Huifeng Yao

Amirsaeed Yazdani

Michael Yip

Hwa-Seong Yoo

Fereshteh Yousefirizi

Shu-Fen Yu

Lei Yu

Jonathan Zamora

Ramy Ashraf Zeineldin

Dewen Zeng

Jianpeng Zhang

Bokai Zhang

Jiapeng Zhang

Fangxi Zhang

Huahong Zhang

Zhongchen Zhao

Zixuan Zhao

Jia Zhao

Can Zhao

Q. Zheng

Yuheng Zhi

Ziqi Zhou

Baosheng Zou

Klaus Maier-Hein

PAUL F. JÄGER

Annette Kopp-Schneider

Lena Maier-Hein

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practic… (see more)e. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

2022-12-16

ArXiv (preprint)

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann

Annika Reinke

Vivienn Weru

Minu Dietlinde Tizabi

Fabian Isensee

T. Adler

PATRICK GODAU

Veronika Cheplygina

Michal Kozubek

Sharib Ali

Anubha Gupta

Jan. Kybic

Alison Professor Noble

Carlos Ortiz de Sol'orzano

Samiksha Pachade

Caroline Petitjean

Daniel Sage

Donglai Wei

Elizabeth Wilden

Deepak Alapatt … (see 334 more)

Vincent Andrearczyk

Ujjwal Baid

Spyridon Bakas

Niranjan Balu

Sophia Bano

Vivek Singh Bawa

Jorge Bernal

Sebastian Bodenstedt

Alessandro Casella

Jinwook Choi

Olivier Commowick

M. Daum

Adrien Depeursinge

Reuben Dorent

J. Egger

H. Eichhorn

Sandy Engelhardt

Melanie Ganz

Gabriel Girard

Lasse Donovan Hansen

Mattias Paul Heinrich

Nicholas Heller

Alessa Hering

Arnaud Huaulm'e

Hyunjeong Kim

Bennett Landman

Hongwei Bran Li

Jianning Li

Junfang Ma

Anne L. Martel

Carlos Mart'in-Isla

Bjoern Menze

Chinedu Innocent Nwoye

Valentin Oreiller

Nicolas Padoy

Sarthak Pati

Kelly Payette

Carole H. Sudre

K. V. Wijnen

Armine Vardazaryan

Tom Kamiel Magda Vercauteren

Martin Wagner

Chuanbo Wang

Moi Hoon Yap

Zeyun Yu

Chuner Yuan

Maximilian Zenk

Aneeq Zia

David Zimmerer

Rina Bao

Chanyeol Choi

Andrew Cohen

Oleh Dzyubachyk

Adrian Galdran

Tianyuan Gan

Tianqi Guo

Pradyumna Gupta

M. Haithami

Edward Ho

Ikbeom Jang

Zhili Li

Zheng Luo

Filip Lux

Sokratis Makrogiannis

Dominikus Muller

Young-Tack Oh

Subeen Pang

Constantin Pape

Görkem Polat

Charlotte Rosalie Reed

Kanghyun Ryu

Tim Scherr

Vajira L. Thambawita

Haoyu Wang

Xinliang Wang

Kele Xu

H.-I. Yeh

Doyeob Yeo

Yi Yuan

Yan Zeng

Xingwen Zhao

Julian Ronald Abbing

Jannes Adam

Nagesh Adluru

Niklas Agethen

S. Ahmed

Yasmina Al Khalil

Mireia Alenya

Esa J. Alhoniemi

C. An

Talha E Anwar

Tewodros Arega

Netanell Avisdris

D. Aydogan

Yi-Shi Bai

Maria Baldeon Calisto

Berke Doga Basaran

Marcel Beetz

Cheng Bian

Hao-xuan Bian

Kevin Blansit

Louise Bloch

Robert Bohnsack

Sara Bosticardo

J. Breen

Mikael Brudfors

Raphael Brungel

Mariano Cabezas

Alberto Cacciola

Zhiwei Chen

Yucong Chen

Dan Chen

Minjeong Cho

Min-Kook Choi

Chuantao Xie Chuantao Xie

Dana Cobzas

Julien Cohen-Adad

Jorge Corral Acero

Sujit Kumar Das

Marcela de Oliveira

Hanqiu Deng

Guiming Dong

Lars Doorenbos

Cory Efird

Di Fan

Mehdi Fatan Serj

Alexandre Fenneteau

Lucas Fidon

Patryk Filipiak

Ren'e Finzel

Nuno Renato Freitas

C. Friedrich

Mitchell J. Fulton

Finn Gaida

Francesco Galati

Christoforos Galazis

Changna Gan

Zheyao Gao

Sheng Gao

Matej Gazda

Beerend G. A. Gerats

Neil Getty

Adam Gibicar

Ryan J. Gifford

Sajan Gohil

Maria Grammatikopoulou

Daniel Grzech

Orhun Guley

Timo Gunnemann

Chun-Hai Guo

Sylvain Guy

Heonjin Ha

Luyi Han

Ilseok Han

Ali Hatamizadeh

Tianhai He

Ji-Wu Heo

Sebastian Hitziger

SeulGi Hong

Seungbum Hong

Rian Huang

Zi-You Huang

Markus Huellebrand

Stephan Huschauer

M. Hussain

Tomoo Inubushi

Ece Isik Polat

Mojtaba Jafaritadi

Seonghun Jeong

Bailiang Jian

Yu Jiang

Zhifan Jiang

Yu Jin

Smriti Joshi

A. Kadkhodamohammadi

R. A. Kamraoui

Inhak Kang

Jun-Su Kang

Davood Karimi

April Ellahe Khademi

Muhammad Irfan Khan

Suleiman A. Khan

Rishab Khantwal

Kwang-Ju Kim

Timothy Lee Kline

Satoshi Kondo

Elina Kontio

Adrian Krenzer

Artem Kroviakov

Hugo J. Kuijf

Satyadwyoom Kumar

Francesco La Rosa

Abhishek Lad

Doohee Lee

Minho Lee

Chiara Lena

Hao Li

Ling Li

Xingyu Li

F. Liao

Kuan-Ya Liao

Arlindo L. Oliveira

Chaonan Lin

Shanhai Lin

Akis Linardos

M. Linguraru

Han Liu

Tao Liu

Dian Liu

Yanling Liu

Joao Lourencco-Silva

Jing Lu

Jia Lu

Imanol Luengo

Christina Bach Lund

Huan Minh Luu

Yingqi Lv

Uzay Macar

Leon Maechler

L. SinaMansour

Kenji Marshall

Moona Mazher

Richard McKinley

Alfonso Medela

Felix Meissen

Mingyuan Meng

Dylan Bradley Miller

S. Mirjahanmardi

Arnab Kumar Mishra

Samir Mitha

Hassan Mohy-ud-Din

Tony C. W. Mok

Gowtham Krishnan Murugesan

Enamundram Naga Karthik

Sahil Nalawade

Jakub Nalepa

M. Naser

Ramin Nateghi

Hammad Naveed

Quang-Minh Nguyen

Cuong Nguyen Quoc

Brennan Nichyporuk

Bruno Oliveira

David Owen

Jimut Bahan Pal

Junwen Pan

W. Pan

Winnie Pang

Bogyu Park

Vivek G. Pawar

K. Pawar

Michael Peven

Lena Philipp

Tomasz Pieciak

Szymon S Płotka

Marcel Plutat

Fattane Pourakpour

Domen Prelovznik

K. Punithakumar

Abdul Qayyum

Sandro Queir'os

Arman Rahmim

Salar Razavi

Jintao Ren

Mina Rezaei

Jonathan Adam Rico

ZunHyan Rieu

Markus Rink

Johannes Roth

Yusely Ruiz-gonzalez

Numan Saeed

Anindo Saha

Mostafa M. Sami Salem

Ricardo Sanchez-matilla

Kurt G Schilling

Weizhen Shao

Zhiqiang Shen

Ruize Shi

Pengcheng Shi

Daniel Sobotka

Th'eodore Soulier

Bella Specktor Fadida

D. Stoyanov

Timothy Sum Hon Mun

Xiao-Fu Sun

Rong Tao

Franz Thaler

Antoine Th'eberge

Felix Thielke

Helena R. Torres

K. Wahid

Jiacheng Wang

Yifei Wang

W. Wang

Xiong Jun Wang

Jianhui Wen

Ning Wen

Marek Wodziński

Yehong Wu

Fangfang Xia

Tianqi Xiang

Cheng Xiaofei

Lizhang Xu

Tingting Xue

Yu‐Xia Yang

Lingxian Yang

Kai Yao

Huifeng Yao

Amirsaeed Yazdani

Michael Yip

Hwa-Seong Yoo

Fereshteh Yousefirizi

Shu-Fen Yu

Lei Yu

Jonathan Zamora

Ramy Ashraf Zeineldin

Dewen Zeng

Jianpeng Zhang

Bokai Zhang

Jiapeng Zhang

Fangxi Zhang

Huahong Zhang

Zhongchen Zhao

Zixuan Zhao

Jia Zhao

Can Zhao

Q. Zheng

Yuheng Zhi

Ziqi Zhou

Baosheng Zou

Klaus Maier-Hein

PAUL F. JÄGER

Annette Kopp-Schneider

Lena Maier-Hein

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practic… (see more)e. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

2022-12-16

ArXiv (preprint)