{"id":15049,"date":"2024-01-18T10:38:44","date_gmt":"2024-01-18T09:38:44","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=15049"},"modified":"2024-01-18T10:38:44","modified_gmt":"2024-01-18T09:38:44","slug":"why-is-the-winner-the-best","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/why-is-the-winner-the-best\/","title":{"rendered":"Why is the winner the best?"},"content":{"rendered":"\n

Matthias Eisenmann,\u00a0Annika Reinke,\u00a0Vivienn Weru,\u00a0Minu Dietlinde Tizabi,\u00a0Fabian Isensee,\u00a0Tim J. Adler,\u00a0Sharib Ali,\u00a0Vincent Andrearczyk,\u00a0Marc Aubreville,\u00a0Ujjwal Baid,\u00a0Spyridon Bakas,\u00a0Niranjan Balu,\u00a0Sophia Bano,\u00a0Jorge Bernal,\u00a0Sebastian Bodenstedt,\u00a0Alessandro Casella,\u00a0Veronika Cheplygina,\u00a0Marie Daum,\u00a0Marleen de Bruijne,\u00a0Adrien Depeursinge,\u00a0Reuben Dorent,\u00a0Jan Egger,\u00a0David G. Ellis,\u00a0Sandy Engelhardt,\u00a0Melanie Ganz,\u00a0Noha Ghatwary,\u00a0Gabriel Girard,\u00a0Patrick Godau,\u00a0Anubha Gupta,\u00a0Lasse Hansen,\u00a0Kanako Harada,\u00a0Mattias Heinrich,\u00a0Nicholas Heller,\u00a0Alessa Hering,\u00a0Arnaud Huaulm\u00e9,\u00a0Pierre Jannin,\u00a0Ali Emre Kavur,\u00a0Old\u0159ich Kodym,\u00a0Michal Kozubek,\u00a0Jianning Li,\u00a0Hongwei Li,\u00a0Jun Ma,\u00a0Carlos Mart\u00edn-Isla,\u00a0Bjoern Menze,\u00a0Alison Noble,\u00a0Valentin Oreiller,\u00a0Nicolas Padoy,\u00a0Sarthak Pati,\u00a0Kelly Payette,\u00a0Tim R\u00e4dsch,\u00a0Jonathan Rafael-Pati\u00f1o,\u00a0Vivek Singh Bawa,\u00a0Stefanie Speidel,\u00a0Carole H. Sudre,\u00a0Kimberlin van Wijnen,\u00a0Martin Wagner,\u00a0Donglai Wei,\u00a0Amine Yamlahi,\u00a0Moi Hoon Yap,\u00a0Chun Yuan,\u00a0Maximilian Zenk,\u00a0Aneeq Zia,\u00a0David Zimmerer,\u00a0Dogu Baran Aydogan,\u00a0Binod Bhattarai,\u00a0Louise Bloch,\u00a0Raphael Br\u00fcngel,\u00a0Jihoon Cho,\u00a0Chanyeol Choi,\u00a0Qi Dou,\u00a0Ivan Ezhov,\u00a0Christoph M. Friedrich,\u00a0Clifton Fuller,\u00a0Rebati Raman Gaire,\u00a0Adrian Galdran,\u00a0\u00c1lvaro Garc\u00eda Faura,\u00a0Maria Grammatikopoulou,\u00a0SeulGi Hong,\u00a0Mostafa Jahanifar,\u00a0Ikbeom Jang,\u00a0Abdolrahim Kadkhodamohammadi,\u00a0Inha Kang,\u00a0Florian Kofler,\u00a0Satoshi Kondo,\u00a0Hugo Kuijf,\u00a0Mingxing Li,\u00a0Minh Huan Luu,\u00a0Toma\u017e Martin\u010di\u010d,\u00a0Pedro Morais,\u00a0Mohamed A. Naser,\u00a0Bruno Oliveira,\u00a0David Owen,\u00a0Subeen Pang,\u00a0Jinah Park,\u00a0Sung-Hong Park,\u00a0Szymon P\u0142otka,\u00a0Elodie Puybareau,\u00a0Nasir Rajpoot,\u00a0Kanghyun Ryu,\u00a0Numan Saeed\u00a0et al. (25 additional authors not shown)<\/h2>\n\n\n\n
<\/div>\n\n\n\n

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common char- acteristics of winning solutions. These typically include the use of multi-task learning (63%) and\/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The \u201ctypical\u201d lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyz- ing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain prob- lem. The insights of our study could help researchers (1) improve algorithm development strategies when approach- ing new problems, and (2) focus on open research questions revealed by this work.<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n","protected":false},"excerpt":{"rendered":"

In: 2023.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-15049","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"\nWhy is the winner the best? - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/why-is-the-winner-the-best\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why is the winner the best?\" \/>\n<meta property=\"og:description\" content=\"In: 2023.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/why-is-the-winner-the-best\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/why-is-the-winner-the-best\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/why-is-the-winner-the-best\\\/\",\"name\":\"Why is the winner the best? 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Adler,\u00a0Sharib Ali,\u00a0Vincent Andrearczyk,\u00a0Marc Aubreville,\u00a0Ujjwal Baid,\u00a0Spyridon Bakas,\u00a0Niranjan Balu,\u00a0Sophia Bano,\u00a0Jorge Bernal,\u00a0Sebastian Bodenstedt,\u00a0Alessandro Casella,\u00a0Veronika Cheplygina,\u00a0Marie Daum,\u00a0Marleen de Bruijne,\u00a0Adrien Depeursinge,\u00a0Reuben Dorent,\u00a0Jan Egger,\u00a0David G. Ellis,\u00a0Sandy Engelhardt,\u00a0Melanie Ganz,\u00a0Noha Ghatwary,\u00a0Gabriel Girard,\u00a0Patrick Godau,\u00a0Anubha Gupta,\u00a0Lasse Hansen,\u00a0Kanako Harada,\u00a0Mattias Heinrich,\u00a0Nicholas Heller,\u00a0Alessa Hering,\u00a0Arnaud Huaulm\u00e9,\u00a0Pierre Jannin,\u00a0Ali Emre Kavur,\u00a0Old\u0159ich Kodym,\u00a0Michal Kozubek,\u00a0Jianning Li,\u00a0Hongwei Li,\u00a0Jun Ma,\u00a0Carlos Mart\u00edn-Isla,\u00a0Bjoern Menze,\u00a0Alison Noble,\u00a0Valentin Oreiller,\u00a0Nicolas Padoy,\u00a0Sarthak Pati,\u00a0Kelly Payette,\u00a0Tim R\u00e4dsch,\u00a0Jonathan Rafael-Pati\u00f1o,\u00a0Vivek Singh Bawa,\u00a0Stefanie Speidel,\u00a0Carole H. Sudre,\u00a0Kimberlin van Wijnen,\u00a0Martin Wagner,\u00a0Donglai Wei,\u00a0Amine Yamlahi,\u00a0Moi Hoon Yap,\u00a0Chun Yuan,\u00a0Maximilian Zenk,\u00a0Aneeq Zia,\u00a0David Zimmerer,\u00a0Dogu Baran Aydogan,\u00a0Binod Bhattarai,\u00a0Louise Bloch,\u00a0Raphael Br\u00fcngel,\u00a0Jihoon Cho,\u00a0Chanyeol Choi,\u00a0Qi Dou,\u00a0Ivan Ezhov,\u00a0Christoph M. Friedrich,\u00a0Clifton Fuller,\u00a0Rebati Raman Gaire,\u00a0Adrian Galdran,\u00a0\u00c1lvaro Garc\u00eda Faura,\u00a0Maria Grammatikopoulou,\u00a0SeulGi Hong,\u00a0Mostafa Jahanifar,\u00a0Ikbeom Jang,\u00a0Abdolrahim Kadkhodamohammadi,\u00a0Inha Kang,\u00a0Florian Kofler,\u00a0Satoshi Kondo,\u00a0Hugo Kuijf,\u00a0Mingxing Li,\u00a0Minh Huan Luu,\u00a0Toma\u017e Martin\u010di\u010d,\u00a0Pedro Morais,\u00a0Mohamed A. Naser,\u00a0Bruno Oliveira,\u00a0David Owen,\u00a0Subeen Pang,\u00a0Jinah Park,\u00a0Sung-Hong Park,\u00a0Szymon P\u0142otka,\u00a0Elodie Puybareau,\u00a0Nasir Rajpoot,\u00a0Kanghyun Ryu,\u00a0Numan Saeed\u00a0et al. (25 additional authors not shown)<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">Matthias Eisenmann,\u00a0Annika Reinke,\u00a0Vivienn Weru,\u00a0Minu Dietlinde Tizabi,\u00a0Fabian Isensee,\u00a0Tim J. Adler,\u00a0Sharib Ali,\u00a0Vincent Andrearczyk,\u00a0Marc Aubreville,\u00a0Ujjwal Baid,\u00a0Spyridon Bakas,\u00a0Niranjan Balu,\u00a0Sophia Bano,\u00a0Jorge Bernal,\u00a0Sebastian Bodenstedt,\u00a0Alessandro Casella,\u00a0Veronika Cheplygina,\u00a0Marie Daum,\u00a0Marleen de Bruijne,\u00a0Adrien Depeursinge,\u00a0Reuben Dorent,\u00a0Jan Egger,\u00a0David G. Ellis,\u00a0Sandy Engelhardt,\u00a0Melanie Ganz,\u00a0Noha Ghatwary,\u00a0Gabriel Girard,\u00a0Patrick Godau,\u00a0Anubha Gupta,\u00a0Lasse Hansen,\u00a0Kanako Harada,\u00a0Mattias Heinrich,\u00a0Nicholas Heller,\u00a0Alessa Hering,\u00a0Arnaud Huaulm\u00e9,\u00a0Pierre Jannin,\u00a0Ali Emre Kavur,\u00a0Old\u0159ich Kodym,\u00a0Michal Kozubek,\u00a0Jianning Li,\u00a0Hongwei Li,\u00a0Jun Ma,\u00a0Carlos Mart\u00edn-Isla,\u00a0Bjoern Menze,\u00a0Alison Noble,\u00a0Valentin Oreiller,\u00a0Nicolas Padoy,\u00a0Sarthak Pati,\u00a0Kelly Payette,\u00a0Tim R\u00e4dsch,\u00a0Jonathan Rafael-Pati\u00f1o,\u00a0Vivek Singh Bawa,\u00a0Stefanie Speidel,\u00a0Carole H. Sudre,\u00a0Kimberlin van Wijnen,\u00a0Martin Wagner,\u00a0Donglai Wei,\u00a0Amine Yamlahi,\u00a0Moi Hoon Yap,\u00a0Chun Yuan,\u00a0Maximilian Zenk,\u00a0Aneeq Zia,\u00a0David Zimmerer,\u00a0Dogu Baran Aydogan,\u00a0Binod Bhattarai,\u00a0Louise Bloch,\u00a0Raphael Br\u00fcngel,\u00a0Jihoon Cho,\u00a0Chanyeol Choi,\u00a0Qi Dou,\u00a0Ivan Ezhov,\u00a0Christoph M. Friedrich,\u00a0Clifton Fuller,\u00a0Rebati Raman Gaire,\u00a0Adrian Galdran,\u00a0\u00c1lvaro Garc\u00eda Faura,\u00a0Maria Grammatikopoulou,\u00a0SeulGi Hong,\u00a0Mostafa Jahanifar,\u00a0Ikbeom Jang,\u00a0Abdolrahim Kadkhodamohammadi,\u00a0Inha Kang,\u00a0Florian Kofler,\u00a0Satoshi Kondo,\u00a0Hugo Kuijf,\u00a0Mingxing Li,\u00a0Minh Huan Luu,\u00a0Toma\u017e Martin\u010di\u010d,\u00a0Pedro Morais,\u00a0Mohamed A. Naser,\u00a0Bruno Oliveira,\u00a0David Owen,\u00a0Subeen Pang,\u00a0Jinah Park,\u00a0Sung-Hong Park,\u00a0Szymon P\u0142otka,\u00a0Elodie Puybareau,\u00a0Nasir Rajpoot,\u00a0Kanghyun Ryu,\u00a0Numan Saeed\u00a0et al. (25 additional authors not shown)<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-koslDw","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-SRn8tU","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common char- acteristics of winning solutions. These typically include the use of multi-task learning (63%) and\/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The \u201ctypical\u201d lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyz- ing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain prob- lem. The insights of our study could help researchers (1) improve algorithm development strategies when approach- ing new problems, and (2) focus on open research questions revealed by this work.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common char- acteristics of winning solutions. These typically include the use of multi-task learning (63%) and\/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The \u201ctypical\u201d lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyz- ing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain prob- lem. The insights of our study could help researchers (1) improve algorithm development strategies when approach- ing new problems, and (2) focus on open research questions revealed by this work.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-koslDw","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/openaccess.thecvf.com\/content\/CVPR2023\/papers\/Eisenmann_Why_Is_the_Winner_the_Best_CVPR_2023_paper.pdf","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15049","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":2,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15049\/revisions"}],"predecessor-version":[{"id":15051,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15049\/revisions\/15051"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=15049"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=15049"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=15049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}