output
20142022
most citedParameterized Explainer for Graph Neural Network

213 citations

5 papers

physics.med-ph20221 cited

Deep Learning-based Protoacoustic Signal Denoising for Proton Range Verification

Jing Wang, James J. Sohn, Yang Lei +5

Objective: Proton therapy offers an advantageous dose distribution compared to the photon therapy, since it deposits most of the energy at the end of range, namely the Bragg peak (…

cs.LG2022

Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging

Hongmei Zhang, Kai Wang, Yan Zhou +4

Objective: Estimation of the global optima of multiple model parameters is valuable in imaging to form a reliable diagnostic image. Given non convexity of the objective function, i…

eess.IV2021

Cell abundance aware deep learning for cell detection on highly imbalanced pathological data

Yeman Brhane Hagos, Catherine SY Lecat, Dominic Patel +5

Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital patho…

cs.LG2020213 cited

Parameterized Explainer for Graph Neural Network

Dongsheng Luo, Wei Cheng, Dongkuan Xu +4

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the loca…

stat.AP2014

Leveraging local identity-by-descent increases the power of case/control GWAS with related individuals

Joshua N. Sampson, Bill Wheeler, Peng Li +1

Large case/control Genome-Wide Association Studies (GWAS) often include groups of related individuals with known relationships. When testing for associations at a given locus, curr…