Clinton J. Wang

According to our database, Clinton J. Wang authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

InterNeRF: Scaling Radiance Fields via Parameter Interpolation.

Clinton J. Wang, Peter Hedman, Polina Golland, Jonathan T. Barron, Daniel Duckworth

CoRR, 2024

Implicit Representations via Operator Learning.

Sourav Pal, Harshavardhan Adepu, Clinton J. Wang, Polina Golland, Vikas Singh

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Spatial-Intensity Transforms for Medical Image-to-Image Translation.

Clinton J. Wang, Natalia S. Rost, Polina Golland

IEEE Trans. Medical Imaging, November, 2023

Discretization Invariant Networks for Learning Maps between Neural Fields.

Clinton J. Wang, Polina Golland

Trans. Mach. Learn. Res., 2023

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.

S. Mazdak Abulnaga, Neel Dey, Sean I. Young, Eileen Pan, Katherine I. Hobgood, Clinton J. Wang, Patricia Ellen Grant, Esra Abaci Turk, Polina Golland

CoRR, 2023

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series.

Zeen Chi, Zhongxiao Cong, Clinton J. Wang, Yingcheng Liu, Esra Abaci Turk, Patricia Ellen Grant, S. Mazdak Abulnaga, Polina Golland, Neel Dey

CoRR, 2023

Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation.

Dominik Hollidt, Clinton J. Wang, Polina Golland, Marc Pollefeys

CoRR, 2023

Interpolating between Images with Diffusion Models.

Clinton J. Wang, Polina Golland

CoRR, 2023

2022

Deep Learning on Implicit Neural Datasets.

Clinton J. Wang, Polina Golland

CoRR, 2022

Automatic Segmentation of the Placenta in BOLD MRI Time Series.

S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood, Eileen Pan, Clinton J. Wang, Patricia Ellen Grant, Esra Abaci Turk, Polina Golland

Proceedings of the Perinatal, Preterm and Paediatric Image Analysis, 2022

2020

Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation.

Clinton J. Wang, Natalia S. Rost, Polina Golland

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019

A probabilistic approach for interpretable deep learning in liver cancer diagnosis.

Clinton J. Wang, Charlie A. Hamm, Brian S. Letzen, James S. Duncan

Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019