Lymphoma

Iterative single-cell multi-omic integration using online learning.

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memor...

Deep learning-based attenuation correction for brain PET with various radiotracers.

OBJECTIVES: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomograp...

Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence.

After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these st...

Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.

Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessmen...

Application of Artificial Neural Network as a nonhazardous alternative on kinetic analysis and modeling for green synthesis of cobalt nanocatalyst from Ocimum tenuiflorum.

The present paper is dedicated to analyze non-hazardous kinetic behaviour and modelling of green synthesized cobalt nanocatalyst (CoNCs), using an Art...

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies.

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis.

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignifi...

Sign function and ANN based pole placement for computing interval controls.

This paper deals with estimation of interval controls for interval Linear Time Invariant (LTI) plants using pole placement technique. Generally, the p...

Should Peritoneal Re-Approximation Be Performed After Transperitoneal Robot-Assisted Radical Prostatectomy?

The aim of the study is to examine the effect of peritoneal re-approximation or non-approximation on the postoperative course of patients at the end ...

Data-efficient and weakly supervised computational pathology on whole-slide images.

Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs wi...

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of A...

Weakly supervised deep learning for determining the prognostic value of F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

PURPOSE: To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of ex...

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data.

Most biomedical datasets, including those of 'omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, th...

Cerebral blood flow measurements with O-water PET using a non-invasive machine-learning-derived arterial input function.

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of O-labeled water by using tracer kinetic modelling. Howeve...

Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.

PURPOSE: Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation p...

Diagnosis of COVID-19 using CT scan images and deep learning techniques.

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over...

Denoising non-steady state dynamic PET data using a feed-forward neural network.

The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromis...

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effec...

Dynamics of Systemic Inflammation as a Function of Developmental Stage in Pediatric Acute Liver Failure.

The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infants and children diagnosed with this complex clinic...

Recent advances in medical image processing for the evaluation of chronic kidney disease.

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imagi...