Search Results for author:
Found 7 papers, 4 papers with code
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
Label-Efficient Multi-Task Segmentation using Contrastive Learning
Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks.
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation
To evaluate the applicability of the ITL approach, we adopted the brain tissue annotation label on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the task of brain tumor segmentation on the target domain dataset of MRI.
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs).
GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention
We try to improve the visual image quality of the CT reconstruction using Guided Attention based GANs (GA-GAN).
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs).
BayesGrad: Explaining Predictions of Graph Convolutional Networks
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.
