Search Results for author:
Found 11 papers, 7 papers with code
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.
Medical Image Segmentation with Domain Adaptation: A Survey
Our goal was to provide researchers with up-to-date references on the applications of domain adaptation in medical image segmentation studies.
Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI).
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.
Context-endcoding for neural network based skull stripping in magnetic resonance imaging
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images.
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation
In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.
Automated Pulmonary Nodule Detection And Classification
Computed Tomography (CT)
+3
A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography.
On Spectral Analysis of Directed Signed Graphs
It has been shown that the adjacency eigenspace of a network contains key information of its underlying structure.
