arXiv AIBy 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
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
arXiv:2603. 01250v2 Announce Type: replace-cross Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality.
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Team FME submitted a method for the MAMA-MIA Challenge that tackles primary tumor segmentation and pathological complete response (pCR) prediction using dynamic contrast‑enhanced breast MRI. For segmentation, they employed a five‑fold residual‑encoder nnU‑Net ensemble trained on the first post‑contrast minus pre‑contrast image, augmented with mirroring test‑time augmentation and largest‑connected‑component filtering, achieving a Dice score of 0.713 and a normalized Hausdorff distance of 0.099. For pCR prediction, they ensembled 25 pretrained 3D video classifiers on lesion‑centred crops from the pre‑contrast and first two post‑contrast volumes, reaching a balanced accuracy of 0.541 and an equalized‑odds disparity of 0.212, and ranked second in both tasks.
arXiv:2609.26105v1 Announce Type: cross
Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...
By Pablo Garc\'ia Marcos, Paula Puerta Gonz\'alez, Guillermo Lorenzo, H\'ector G\'omez, Covadonga del Camino, Ad\'an Rodr\'iguez, Ignacio Pel\'aez, Angel Rio-Alvarez, V\'ictor M. Gonz\'alez
arXiv:2609.26443v1 Announce Type: new
Abstract: Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, dia...
By Farnoush Bayatmakou, Maryam Hosseini, Reza Taleei, Arash Mohammadi
arXiv:2607. 19137v1 Announce Type: cross Abstract: Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy.
By Andrea Borghesi, Xin Wang, Jonas Teuwen, George Yiasemis
arXiv:2512. 17605v2 Announce Type: replace-cross Abstract: Robust mammography registration is essential for clinically relevant applications like tracking disease progression in breast tissue.
By Svetlana Krasnova, Emiliya Starikova, Ilia Naletov, Andrey Krylov, Dmitry Sorokin