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The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
arXiv – CS AI|Lidia Garrucho, Smriti Joshi, Kaisar Kushibar, Richard Osuala, Maciej Bobowicz, Xavier Bargall\'o, Paulius Jaru\v{s}evi\v{c}ius, Kai Geissler, Raphael Sch\"afer, Muhammad Alberb, Tony Xu, Anne Martel, Daniel Sleiman, Navchetan Awasthi, Hadeel Awwad, Joan C. Vilanova, Robert Mart\'i, Daan Schouten, Jeong Hoon Lee, Mirabela Rusu, Eleonora Poeta, Luisa Vargas, Eliana Pastor, Maria A. Zuluaga, Jessica K\"achele, Dimitrios Bounias, Alexandra Ertl, Katarzyna Gwo\'zdziewicz, Maria-Laura Cosaka, Pasant M. Abo-Elhoda, Sara W. Tantawy, Shorouq S. Sakrana, Norhan O. Shawky-Abdelfatah, Amr Muhammad Abdo-Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E. Klontzas, Rosa Garc\'ia-Dosd\'a, Meltem Gulsun-Akpinar, O\u{g}uz Lafc{\i}, Carlos Mart\'in-Isla, Oliver D\'iaz, Laura Igual, Karim Lekadir||4 views
🤖AI Summary
The MAMA-MIA Challenge introduced a large-scale benchmark for AI-powered breast cancer tumor segmentation and treatment response prediction using MRI data from 1,506 US patients for training and 574 European patients for testing. Results from 26 international teams revealed significant performance variability and trade-offs between accuracy and fairness across demographic subgroups when AI models were tested across different institutions and continents.
Key Takeaways
- →A major AI challenge addressed limitations in breast cancer MRI analysis by testing cross-continental generalizability with US training data and European test data.
- →26 international teams participated, revealing substantial performance drops when AI models were applied to external datasets from different institutions.
- →The challenge highlighted critical trade-offs between overall accuracy and fairness across age, menopausal status, and breast density subgroups.
- →Single-center AI model development was identified as a key limitation preventing real-world deployment in medical imaging.
- →Standardized datasets and evaluation protocols were established to promote development of more robust and equitable AI systems for cancer diagnosis.
#artificial-intelligence#medical-ai#breast-cancer#machine-learning#healthcare#mri-imaging#ai-fairness#benchmark#cross-institutional
Read Original →via arXiv – CS AI
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