We envision the incorporation of fuzzy labels into Semi-Supervised Learning and give a proof-of-concept of the potential lower costs and higher consistency in the complete development cycle.
We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels.
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation.
Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning.
We propose a novel loss to improve the overclustering capability of our framework and show on the common image classification dataset STL-10 that it is faster and has better overclustering performance than previous work.
Quantitative computed tomography (QCT) permits the selective analysis of cortical bone, however the low spatial resolution of clinical QCT leads to an overestimation of the thickness of cortical bone (Ct. Th) and bone strength.
In recent years, methods based on deep learning have been introduced and have shown pleasingly good results.
By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator and allows experts to adapt the granularity of their sorting scheme to the structure in the data.
Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain.
In this survey, we provide an overview of often used ideas and methods in image classification with fewer labels.
Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations.
Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring.