Lymphoma

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-...

Motion correction of respiratory-gated PET images using deep learning based image registration framework.

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-ri...

Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation.

Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW ...

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on s...

Improved myocardial perfusion PET imaging using artificial neural networks.

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, w...

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-co...

Towards explainable deep neural networks (xDNN).

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers an ex...

k-hop graph neural networks.

Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same...

New technologies in breast cancer sentinel lymph node biopsy; from the current gold standard to artificial intelligence.

Sentinel lymph node biopsy is an important diagnostic procedure performed in early breast cancer patients with clinically negative axillary lymph node...

F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation.

BACKGROUND: Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medicall...

Deep-Learning F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.

Total metabolic tumor volume (TMTV), calculated from F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) wh...

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning.

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living...

Using Artificial Intelligence for Pattern Recognition in a Sports Context.

Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired u...

Performance improvement of mediastinal lymph node severity detection using GAN and Inception network.

BACKGROUND AND OBJECTIVE: In lung cancer, the determination of mediastinal lymph node (MLN) status as benign or malignant influence treatment planning...

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards.

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the m...

Utilization of a Peritoneal Interposition Flap to Prevent Symptomatic Lymphoceles After Robotic Radical Prostatectomy and Bilateral Pelvic Lymph Node Dissection.

The peritoneal interposition flap (PIF) has been shown to prevent postoperative symptomatic lymphocele (SL) formation after robot-assisted radical pr...

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challe...

Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.

The purpose of this study was to assess, by analyzing features of the primary tumor with F-FDG PET, the utility of deep machine learning with a convo...

Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.

OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique.

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has re...