用掩码引导的多通道SwinUNETR提升乳腺MRI分类准确率
Mask-Guided Multi-Channel SwinUNETR Framework for Robust MRI Classification
- 引入乳腺区域掩码增强特征提取，提升模型聚焦能力
- 在6个中心511例数据上达第二名，表现稳定可靠
- 适合医学影像领域需跨中心泛化的研究者使用
乳腺癌是女性癌症死亡的主要原因，早期检测对改善预后至关重要。磁共振成像（MRI）对高危或致密乳腺组织女性具有高灵敏度，是哺乳检查效果不佳时的重要替代手段。ODELIA联盟组织了多中心挑战赛，推动基于AI的乳腺癌诊断与分类方法发展。数据集包含来自欧洲六家中心的511例扫描，使用1.5T与3T多品牌设备采集，每例左右乳腺分别标注为无病灶、良性病灶或恶性病灶。我们提出一种基于SwinUNETR的深度学习框架，融合乳腺区域掩码、大规模数据增强和集成学习策略，显著提升模型鲁棒性与泛化能力。该方法在挑战赛中位列第二，展现出辅助临床乳腺MRI判读的潜力。代码已公开：https://github.com/smriti-joshi/bcnaim-odelia-challenge.git。
原文摘要 · Abstract (English)
Breast cancer is one of the leading causes of cancer-related mortality in women, and early detection is essential for improving outcomes. Magnetic resonance imaging (MRI) is a highly sensitive tool for breast cancer detection, particularly in women at high risk or with dense breast tissue, where mammography is less effective. The ODELIA consortium organized a multi-center challenge to foster AI-based solutions for breast cancer diagnosis and classification. The dataset included 511 studies from six European centers, acquired on scanners from multiple vendors at both 1.5 T and 3 T. Each study was labeled for the left and right breast as no lesion, benign lesion, or malignant lesion. We developed a SwinUNETR-based deep learning framework that incorporates breast region masking, extensive data augmentation, and ensemble learning to improve robustness and generalizability. Our method achieved second place on the challenge leaderboard, highlighting its potential to support clinical breast MRI interpretation. We publicly share our codebase at https://github.com/smriti-joshi/bcnaim-odelia-challenge.git.
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