Search Results for author:
Found 6 papers, 2 papers with code
Denoising Diffusion Medical Models
In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis.
Neural Radiance Projection
The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution.
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation
Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision.
Reinforced Coloring for End-to-End Instance Segmentation
To exploit the advantages of conventional single-object-per-step segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel.
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.
