Lymphoma

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and artificial neural networks to denoise dynamic PET images. We train a deep denoising autoencoder (DAE) using noisy and noise-free spatiotemporal image p...

A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization.

The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revascularization and from 3538 non-ACS patients. We analyzed 20 features that are relevant to ACS using standard algorithms, support vector machines and li...

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 treatme...

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...

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...

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingl...

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 ...

Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD...

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...