Search Results for author:
Found 9 papers, 3 papers with code
Multi-Objective Learning for Deformable Image Registration
The experiments also show that the proposed MO DIR approach provides a better spread of DIR outputs across the entire trade-off front than simply training multiple neural networks with weights for each objective sampled from a grid of possible values.
Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations
Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations.
Mixed-Block Neural Architecture Search for Medical Image Segmentation
The design of the best possible medical image segmentation DNNs, however, is task-specific.
Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration
Conclusions: DCNN-Match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization
We discuss and illustrate why training processes to approximate Pareto fronts need to optimize on fronts of individual training samples instead of on only the front of average losses.
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
We tested the approach on 22, 206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations.
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases.
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans.
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level.
