Lymphoma

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to identify the stable and distinguishable characteristics of lymphoma and GBM in F-fluorodeocxyglucose (FDG) positron emission tomography (PET) images ...

Application of artificial neural networks for Process Analytical Technology-based dissolution testing.

This work proposes the application of artificial neural networks (ANN) to non-destructively predict the in vitro dissolution of pharmaceutical tablets from Process Analytical Technology (PAT) data. An extended release tablet formulation was studied, where the dissolution was influenced by the composition of the tablets and the tableting compression force. NIR and Raman spectra of the intact tablet...

Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodie...

Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin ...

Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma.

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow bio...

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly...

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and rep...

Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-b...

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between ...

Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts.

Non-conventional scan trajectories for interventional three-dimensional imaging promise low-dose interventions and a better radiation protection to th...

Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning.

Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeon...

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumo...

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed ...

Post-chemotherapy robot-assisted retroperitoneal lymph node dissection in non-seminomatous germ cell tumor of testis: Feasibility and outcomes of initial cases.

OBJECTIVE: To report our initial experience and short-term results in post-chemotherapy robot-assisted retroperitoneal lymph node dissection (RA-RPLND...

Deep learning in spiking neural networks.

In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilay...

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks.

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who wil...

Particulate Matter Exposure of Passengers at Bus Stations: A Review.

This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passe...

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns.

Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over tradi...

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network.

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of sm...

Genotoxicity evaluation of titanium dioxide nanoparticles using the mouse lymphoma assay and the Ames test.

Titanium dioxide nanoparticles (TiO-NPs) are widely used in the cosmetics, health, and food industries, but their safety and genotoxicity remain a mat...