13 citations · 34 across the 6 of their papers we have counts for
7 papers
Segmentation of Breast Microcalcifications: A Multi-Scale Approach
Chrysostomos Marasinou, Bo Li, Jeremy Paige +4
Accurate characterization of microcalcifications (MCs) in 2D full-field digital screening mammography is a necessary step towards reducing diagnostic uncertainty associated with th…
Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features
Leihao Wei, Yannan Lin, William Hsu
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-…
Discovering and interpreting transcriptomic drivers of imaging traits using neural networks
Nova F. Smedley, Suzie El-Saden, William Hsu
Motivation. Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link or…
OpBerg: Discovering causal sentences using optimal alignments
Justin Wood, Nicholas J. Matiasz, Alcino J. Silva +3
The biological literature is rich with sentences that describe causal relations. Methods that automatically extract such sentences can help biologists to synthesize the literature…
Computed Tomography Image Enhancement using 3D Convolutional Neural Network
Meng Li, Shiwen Shen, Wen Gao +2
Computed tomography (CT) is increasingly being used for cancer screening, such as early detection of lung cancer. However, CT studies have varying pixel spacing due to differences…
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
Shiwen Shen, Simon X. Han, Denise R. Aberle +2
While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowl…