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Abstract #3534

Weakly Supervised Exclusion of Non-Tumoral Enhancement in Low Volume Dataset for Breast Tumor Segmentation

Michael Liu1,2, Richard Ha1, Yu-Cheng Liu1, Tim Duong2, Terry Button2, Pawas Shukla1, and Sachin Jambawalikar1
1Radiology, Columbia University, New York, NY, United States, 2Stony Brook University, Stony Brook, NY, United States

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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