Feature Space Guidance for Breast Cancer Classification in DCE-MRI
Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail together with rich temporal contrast information. However, high dimensional 4D inputs, small lesions, and heterogeneous acquisition protocols across clinical sites hinder robust automated classification of healthy, benign, and malignant cases. To address these challenges, we propose a framework that dynamically analyzes latent representations to adapt to protocol-specific characteristics. Spatial variability is mitigated by reducing confounding background uptake and compensating for misalignment caused by deformable soft tissue. Additionally, ...
| Authors | Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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