1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CV2024★ 1 cited
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
Julia Yang, Alina Jade Barnett, Jon Donnelly +6
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-s…
eess.IV2024
What limits performance of weakly supervised deep learning for chest CT classification?
Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin +1
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about t…