However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments.
In this study, the effect of improper dataset splitting on model evaluation is demonstrated for two classification tasks using two OCT open-access datasets extensively used in the literature, Kermany's ophthalmology dataset and AIIMS breast tissue dataset.
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years.
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
The BraTS2020 dataset was used to train and test two standard 3D U-Net models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP).
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i. e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core.
Deep learning requires large datasets for training (convolutional) networks with millions of parameters.
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology.
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing.
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique.
Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images.
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space.