Lymphoma

A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming.

Drug discovery is one of the major goals of computational biology and bioinformatics. A novel framework has recently been proposed for the design of chemical graphs using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). This method consists of a prediction phase and an inverse prediction phase. In the first phase, an ANN is trained using data on existing chemical...

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras.

The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicle information has become an adventurous task, with frameworks deployed on different domains to scan features independently. Furthermore, approach iden...

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming...

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients wit...

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.

The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using mu...

Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases.

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper devel...

Non-Local Graph Neural Networks.

Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortativ...

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth ...

Fluorescence-guided extended pelvic lymphadenectomy during robotic radical prostatectomy.

We evaluated and described the impact of prostatic indocyanine green (ICG) injection on extended pelvic lymph node (LN) dissection (ePLND) in robotic-...

Comparison of single-port and multi-port Retzius-sparing robot-assisted laparoscopic prostatectomy.

Dissemination of robotic surgical technology for robot-assisted laparoscopic prostatectomy (RALP) has yielded advancements including the Retzius-spari...

Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images.

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential thre...

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home.

Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especiall...

Considerate motion imagination classification method using deep learning.

In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is p...

Adaptive graph convolutional clustering network with optimal probabilistic graph.

The graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the top...

Precision Medicine Approaches with Metabolomics and Artificial Intelligence.

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth...

Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in mu...

Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.

We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TOF information in the image space (IS) and sinogram...

DeepMTS: Deep Multi-Task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT.

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients,...

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identi...

Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms.

Anaplastic lymphoma kinase (ALK) and ROS oncogene 1 (ROS1) gene fusions are well-established key players in non-small cell lung cancer (NSCLC). Althou...