Continual learning approaches for single cell RNA sequencing data.

Single-cell RNA sequencing data is among the most interesting and impactful data of today and the sizes of the available datasets are increasing drastically. There is a substantial need for learning from large datasets, causing nontrivial challenges, especially in hardware. Loading even a single dataset into the memory of an ordinary, off-the-shelf computer can be infeasible, and using computing servers might not always be an option.

Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data.

Arguably one of the most famous dimensionality reduction algorithms of today is t-distributed stochastic neighbor embedding (t-SNE). Although being widely used for the visualization of scRNA-seq data, it is prone to errors as any algorithm and may lead to inaccurate interpretations of the visualized data. A reasonable way to avoid misinterpretations is to quantify the reliability of the visualizations.

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry.

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans.

Exploiting lamina terminalis appearance and motion in prediction of hydrocephalus using convolutional LSTM network.

Background: Evaluation of the lamina terminalis (LT) is crucial for non-invasive evaluation of the CSF diversion for the treatment of hydrocephalus. Together with deep learning algorithms, morphological and physiological analyses of the LT may play an important role in the management of hydrocephalus.

Aim: We aim to show that exploiting the motion of LT can contribute to the evaluation of hydrocephalus using deep learning algorithms.

Comparison of morphometric parameters in prediction of hydrocephalus using random forests.

Ventricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hydrocephalus. In this study, we explored the effectiveness of commonly used morphological parameters in hydrocephalus diagnosis.