Quantitative measures of breast functional tumor volume are important response predictors of breast cancer undergoing chemotherapy. Automated segmentation networks have difficulty excluding non tumoral enhancing structures from their segmentations. Using a small small DCE-MRI dataset with coarse slice level labels to weakly supervised segmentation was able to exclude large portions of non tumor structures. Without manual pixel wise segmentation, our Class activation map based region proposer excluded 67% of non-tumoral voxels in a sagittal slice from downstream segmentation networks while maintaining 94% sensitivity.
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