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Found 6 papers, 0 papers with code
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging
Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed.
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
The Cox neural network can achieve an IPCW C-index of 0. 75 on the internal dataset and 0. 69 on an external dataset.
Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors
This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images.
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Our model demonstrated comparable and complementary performance to radiologists when interpreting challenging lung nodules and showed improved performance (AUC=88\%) against models utilizing single time-point data only.
Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts.
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data.
