36,028 research outputs found
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
The use of neural networks to directly predict three-dimensional dose
distributions for automatic planning is becoming popular. However, the existing
methods only use patient anatomy as input and assume consistent beam
configuration for all patients in the training database. The purpose of this
work is to develop a more general model that, in addition to patient anatomy,
also considers variable beam configurations, to achieve a more comprehensive
automatic planning with a potentially easier clinical implementation, without
the need of training specific models for different beam settings
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on
Data Mining in Biomedical Informatics and Healthcare at ICDM, 201
Learning Optimal Deep Projection of F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
Several diseases of parkinsonian syndromes present similar symptoms at early
stage and no objective widely used diagnostic methods have been approved until
now. Positron emission tomography (PET) with F-FDG was shown to be able
to assess early neuronal dysfunction of synucleinopathies and tauopathies.
Tensor factorization (TF) based approaches have been applied to identify
characteristic metabolic patterns for differential diagnosis. However, these
conventional dimension-reduction strategies assume linear or multi-linear
relationships inside data, and are therefore insufficient to distinguish
nonlinear metabolic differences between various parkinsonian syndromes. In this
paper, we propose a Deep Projection Neural Network (DPNN) to identify
characteristic metabolic pattern for early differential diagnosis of
parkinsonian syndromes. We draw our inspiration from the existing TF methods.
The network consists of a (i) compression part: which uses a deep network to
learn optimal 2D projections of 3D scans, and a (ii) classification part: which
maps the 2D projections to labels. The compression part can be pre-trained
using surplus unlabelled datasets. Also, as the classification part operates on
these 2D projections, it can be trained end-to-end effectively with limited
labelled data, in contrast to 3D approaches. We show that DPNN is more
effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201
- …