Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks.
In this work, we propose a framework to learn a local regularization model for solving general image restoration problems.
Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters.
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
First, a pixel-based optimization method is proposed, relying on discrete optimal transport.
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
This is done through the use of refitting block penalties that only act on the support of the estimated solution.
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
We consider the task of classifying when a significantly reduced amount of labelled data is available.
To measure the regularity aspect, we propose a new global regularity measure (GR), which addresses the non-robustness of state-of-the-art metrics.
In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework.
On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation.
Superpixels have become very popular in many computer vision applications.
During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter.
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.
The proposed framework unifies hard and soft clustering methods for general mixture models.
Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought.
We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory.
The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes.