2 citations · 6 across the 7 of their papers we have counts for
7 papers
NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild
Rishit Dagli, Atsuhiro Hibi, Rahul G. Krishnan +1
Current methods for performing 3D reconstruction and novel view synthesis (NVS) in ultrasound imaging data often face severe artifacts when training NeRF-based approaches. The arti…
Predicting Long-Term Allograft Survival in Liver Transplant Recipients
Xiang Gao, Michael Cooper, Maryam Naghibzadeh +3
Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Provid…
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
Jacob Si, Wendy Yusi Cheng, Michael Cooper +1
Tabular data are omnipresent in various sectors of industries. Neural networks for tabular data such as TabNet have been proposed to make predictions while leveraging the attention…
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
Scheduling laboratory tests for ICU patients presents a significant challenge. Studies show that 20-40% of lab tests ordered in the ICU are redundant and could be eliminated withou…
Structured Neural Networks for Density Estimation and Causal Inference
Asic Q. Chen, Ruian Shi, Xiang Gao +2
Injecting structure into neural networks enables learning functions that satisfy invariances with respect to subsets of inputs. For instance, when learning generative models using…
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Edward De Brouwer, Rahul G. Krishnan
Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a con…