{"id":30,"date":"2023-06-30T15:27:45","date_gmt":"2023-06-30T15:27:45","guid":{"rendered":"https:\/\/www.ub.edu\/mama-mia\/home-2\/"},"modified":"2026-03-13T11:14:08","modified_gmt":"2026-03-13T11:14:08","slug":"home-2","status":"publish","type":"page","link":"https:\/\/www.ub.edu\/mama-mia\/","title":{"rendered":"About"},"content":{"rendered":"\n
\n
\n

Advancing Generalizability<\/strong> and Fairness<\/strong> in Breast MRI Tumour Segmentation and Treatment Response Prediction<\/strong> <\/p>\n<\/div>\n\n\n\n

\n
\n
\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/div>


23 September
Daejeon Convention Center
South Korea<\/mark><\/h2>

Join the MAMA-MIA Challenge<\/mark><\/h3><\/div>
EXPLORE THE DATASET<\/span><\/path><\/svg><\/a><\/div><\/div><\/div>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n
\n
\n
\n
About<\/h6>

Advancing Breast Cancer Care with AI<\/strong><\/h3><\/div>


Breast cancer remains the most common cancer among women and a leading cause of female mortality. Dynamic contrast-enhanced MRI (DCE-MRI) is a powerful imaging tool for evaluating breast tumours, yet the field lacks a standardized benchmark for analyzing treatment responses and guiding personalized care.
<\/p><\/div><\/div>\n\n\n\n

The MAMA-MIA Challenge<\/strong> bridges this gap by introducing a dual-task framework to advance AI-driven solutions for:

1) Primary Tumour Segmentation in DCE-MRI<\/strong>

2) Prediction of Pathologic Complete Response (pCR) to Neoadjuvant Chemotherapy (NAC)<\/strong>

Participants will use the largest homogenized DCE-MRI dataset to date, featuring 1,506 annotated scans and key clinical variables, and validate their models across 574 external cases from diverse imaging protocols and settings. The challenge emphasizes generalizability and fairness, ensuring equitable model performance across demographic subgroups, and aims to optimize breast cancer treatment and surgical planning.

Join us to push the boundaries of personalized and ethical AI in breast cancer care.<\/strong>
<\/p>\n\n\n\n

\n
\"\"
Pre-contrast<\/mark><\/figcaption><\/figure>\n\n\n\n
\"\"
Post-contrast<\/mark><\/figcaption><\/figure>\n<\/figure>\n<\/div>\n\n\n\n
\n