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Keywords: Mammography
| MO-E115-IePD-F8-3 | Leveraging Predictive Uncertainty of a Convolutional Neural Network to Flag Unacceptable Segmentations Z Klanecek1*, H Bosmans2,6, A Studen1,5, M Vrhovec4, D Huff3, B Schott3, Y Kuan2, L Cockmartin6, N Marshall2,6, T Wagner2, K Hertl4, M Krajc4, K Jarm4, R Jeraj1,3,5, (1) Faculty of Mathematics and Physics, Ljubljana, SI, (2) KU Leuven, Leuven, BE, (3) University of Wisconsin - Madison, Madison, WI, (4) Institute Of Oncology Ljubljana, Ljubljana, SI, (5) Jozef Stefan Institute, Ljubljana, SI, (6) UZ Leuven, Leuven, BE |
| MO-PinS-INV2-0 | 3D Mammography J Brown1*, M Radicke2*, T Patel3*, R Iordache4*, (1) University of Florida, Gainesville, FL, (2) Siemens Healthineers, Bayern, Deutschland, DE, (3) Hologic, Inc, Danbury, CT, (4) GE Healthcare, Buc, FR |
| TU-E-201-1 | Keynote Address I Reiser1*, (1) The University of Chicago, Chicago, IL |
| TU-E-201-5 | Secondary Electron Energy Spectrum From High-Energy Contrast-Enhanced Mammography H Grover*, A Sampson, UT Health San Antonio, San Antonio, TX |
| TU-E-201-6 | Use of Radiomic Texture to Characterize Relationships Across the Parenchymal Field in Women with Breast Cancer On FFDMs and Specimen Radiographs N Baughan1*, H Li1, L Lan1, M Embury2, G Whitman2, R El-zein3, I Bedrosian2, M Giger1, (1) University of Chicago, Chicago, IL, (2) MD Anderson Cancer Center, Houston, TX, (3) Houston Methodist Research Institute, Houston, TX |