Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Background: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.

Effects of orthographic transparency on rhyme judgement.

This study investigated the influence of multiliteracy in opaque orthographies on phonological awareness. Using a visual rhyme judgement task in English, we assessed phonological processing in three multilingual and multiliterate populations who were distinguished by the transparency of the orthographies they can read in ( = 135; ages 18-40). The first group consisted of 45 multilinguals literate in English and a transparent Latin orthography like Malay; the second group consisted of 45 multilinguals literate in English and transparent orthographies like Malay and Arabic; and the third group consisted of 45 multilinguals literate in English, transparent orthographies, and Mandarin Chinese, an opaque orthography.

An Innovative Concept of a 3D-Coded Aperture Imaging System Proposed for Early Breast Cancer Detection.

Coded Aperture (CA) imaging has recently been used in nuclear medicine, but still, there is no commercial SPECT imaging camera based on CA for cancer detection. The literature is rich in examples of using the CA for planar and thin 3D imaging. However, thick 3D reconstruction is still challenging for small lesion detection.

Towards efficient Monte Carlo N-Particle simulation of a positron emission tomography (PET) via source volume definition.

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists.

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images.