Local Constraint and Label Embedding Multi-layer Dictionary Learning for Sperm Head Classification

Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in each layer, and the two dictionaries are approximated to each other as much as possible, so as to well exploit the nonlinear structure and discriminability features of the morphology of human sperm heads. In addition, to promote the robustness of the model, the asymmetric Huber loss is adopted in the last layer of LCLM-MDL, which approximates the misclassification error by using the absolute error function. Finally, the experimental results on HuSHeM dataset demonstrate the validity of the LCLM-MDL.

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.

Turnaround density as a probe of the cosmological constant

Spherical collapse predicts that a single value of the turnaround density, meaning the average matter density within the scale on which a structure detaches from the Hubble flow, characterizes all cosmic structures at the same redshift. It was recently shown by Korkidis et al. that this feature persists in complex non-spherical galaxy clusters that have been identified in N-body simulations. Here we show that the low-redshift evolution of the turnaround density constrains the cosmological parameters and it can be used to derive a local constraint on ΩΛ, 0 alone, independent of Ωm, 0. The turnaround density thus offers a promising new method for exploiting upcoming large cosmological datasets.