{"paper":{"title":"The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alexandra Ertl, Amr Muhammad Abdo-Salem, Androniki Kozana, Anne Martel, Carlos Mart\\'in-Isla, Daan Schouten, Daniel Sleiman, Dimitrios Bounias, Eleonora Poeta, Eliana Pastor, Eugen Divjak, Gordana Ivanac, Hadeel Awwad, Jeong Hoon Lee, Jessica K\\\"achele, Joan C. Vilanova, Kai Geissler, Kaisar Kushibar, Karim Lekadir, Katarzyna Gwo\\'zdziewicz, Katerina Nikiforaki, Laura Igual, Lidia Garrucho, Luisa Vargas, Maciej Bobowicz, Maria A. Zuluaga, Maria-Laura Cosaka, Meltem Gulsun-Akpinar, Michail E. Klontzas, Mirabela Rusu, Muhammad Alberb, Navchetan Awasthi, Norhan O. Shawky-Abdelfatah, Oliver D\\'iaz, O\\u{g}uz Lafc{\\i}, Pasant M. Abo-Elhoda, Paulius Jaru\\v{s}evi\\v{c}ius, Raphael Sch\\\"afer, Richard Osuala, Robert Mart\\'i, Rosa Garc\\'ia-Dosd\\'a, Sara W. Tantawy, Shorouq S. Sakrana, Smriti Joshi, Tony Xu, Xavier Bargall\\'o","submitted_at":"2026-03-01T20:06:30Z","abstract_excerpt":"Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imaging plays a central role in tumor characterization and treatment monitoring, particularly in patients receiving neoadjuvant chemotherapy. However, existing artificial intelligence models for breast magnetic resonance imaging are typically developed and evaluated using heterogeneous datasets, study populations, and assessment protocols, making direct comparison difficult and limiting understanding of model robustness ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.01250","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.01250/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}