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Andreas Haunschmidt

ORCID: 0000-0001-9864-5189
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A5004012852
Research Areas
  • Privacy-Preserving Technologies in Data
  • Radiomics and Machine Learning in Medical Imaging
  • Glioma Diagnosis and Treatment

Kepler Universitätsklinikum
 2022-2023

 Federated learning enables big data for rare cancer boundary detection
OPENALEX - Publications 
Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih‐Han Wang and 95 more G. Anthony Reina Patrick Foley А. Д. Груздев Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Kickingereder Gianluca Brugnara Chandrakanth Jayachandran Preetha Felix Sahm Klaus Maier‐Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey D. Rudie Javier Villanueva‐Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John W. Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y. Huang Ken Chang Carmen Balañá Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S. Alexander Joseph S. Lombardo Joshua D. Palmer Adam E. Flanders Adam P. Dicker Haris I. Sair Craig Jones Archana Venkataraman Meirui Jiang Tiffany Y. So Cheng Chen Pheng‐Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A. Vogelbaum J. Ross Mitchell Joaquim M. Farinhas Joseph A. Maldjian Chandan Ganesh Bangalore Yogananda Marco C. Pinho Divya Reddy James Holcomb Benjamin Wagner Benjamin M. Ellingson Timothy F. Cloughesy Catalina Raymond Talia C. Oughourlian Akifumi Hagiwara Chencai Wang Minh‐Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre X. Falcão Samuel Botter Martins Bernardo Corrêa de Almeida Teixeira F Sprenger David Menotti Diego Rafael Lucio Pamela LaMontagne Daniel S. Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization challenging scale (or even not feasible) due various limitations. Federated ML (FL) provides an alternative train accurate generalizable models, only numerical model updates. Here we present findings the largest FL study...

10.1038/s41467-022-33407-5 article EN cc-by Nature Communications 2022-12-05
 Federated Learning Enables Big Data for Rare Cancer Boundary Detection
OPENALEX - Publications 
Sarthak Pati Ujjwal Baid Brandon L. Edwards Micah Sheller Shih‐Han Wang and 95 more G. Anthony Reina Patrick Foley А. Д. Груздев Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Kickingereder Gianluca Brugnara Chandrakanth Jayachandran Preetha Felix Sahm Klaus Maier‐Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey D. Rudie Javier Villanueva‐Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y. Huang Ken Chang Carmen Balañá Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Garima Shukla Spencer Liem Gregory S. Alexander Joseph S. Lombardo Joshua D. Palmer Adam E. Flanders Adam P. Dicker Haris I. Sair Craig Jones Archana Venkataraman Meirui Jiang Tiffany Y. So Cheng Chen Pheng‐Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A. Vogelbaum J Ross Mitchell Joaquim M. Farinhas Joseph A. Maldjian Chandan Ganesh Bangalore Yogananda Marco C. Pinho D V S Reddy James Holcomb Benjamin Wagner Benjamin M. Ellingson Timothy F. Cloughesy Catalina Raymond Talia C. Oughourlian Akifumi Hagiwara Chencai Wang Minh‐Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre X. Falcão Samuel Botter Martins Bernardo Corrêa de Almeida Teixeira F Sprenger David Menotti Diego Rafael Lucio Pamela LaMontagne Daniel C. Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization challenging scale (or even not feasible) due various limitations. Federated ML (FL) provides an alternative train accurate generalizable models, only numerical model updates. Here we present findings the largest FL study...

10.48550/arxiv.2204.10836 preprint EN cc-by arXiv (Cornell University) 2022-01-01
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