Scale-Aware 3D Deep Learning for Robust Brain Metastasis Detection in Multimodal MRI
Detecting brain metastases in magnetic resonance imaging (MRI) remains challenging because lesions vary widely in size and appearance, with very small metastases occupying only a minute fraction of a three-dimensional input. We investigate whether combining different spatial fields of view (FOVs) improves lesion detection in multimodal MRI and present a scale-aware 3D deep-learning framework. The method uses independently trained $96^3$ and $64^3$ 3D U-Nets whose whole-volume probability maps are combined by weighted late fusion. This design allows us to study the effect of spatial context separately from image resolution and modality choice. On a 97-patient development cohort, cross-FOV ...
| Authors | Sylvain Jaume, Hongming Wang, Simon K. Warfield |
|---|---|
| Primary category | eess.IV |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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