Lymphoma

Application of Deep Learning Techniques for Automated Diagnosis of Non-Syndromic Craniosynostosis Using Skull.

Non-syndromic craniosynostosis (NSCS) is a disease, in which a single cranial bone suture is prematurely fused. The early intervention of the disease is associated with a favorable outcome at a later age, so appropriate screening of NSCS is essential for its clinical management. The present study aims to develop a classification and detection system of NSCS using skull X-ray images and a convoluti...

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomi...

Versatile memristor for memory and neuromorphic computing.

The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the characteristic retention time, memris...

Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure.

Although guidelines have recommended standardized drug treatment for heart failure (HF), there are still many challenges in making the correct clinic...

Automated human cell classification in sparse datasets using few-shot learning.

Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an activ...

Segmentation of metastatic cervical lymph nodes from CT images of oral cancers using deep-learning technology.

OBJECTIVE: The purpose of this study was to establish a deep-learning model for segmenting the cervical lymph nodes of oral cancer patients and diagno...

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers.

Within the next 1.5 decades, 1 in 7 U.S. adults is anticipated to suffer from age-related macular degeneration (AMD), a degenerative retinal disease w...

Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images.

OBJECTIVES: To demonstrate the effectiveness of automatic segmentation of diffuse large B-cell lymphoma (DLBCL) in 3D FDG-PET scans using a deep learn...

Subtype classification of malignant lymphoma using immunohistochemical staining pattern.

PURPOSE: For the image classification problem, the construction of appropriate training data is important for improving the generalization ability of ...

Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning.

Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a hea...

Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models' Performance and Robustness.

A small dataset commonly affects generalization, robustness, and overall performance of deep neural networks (DNNs) in medical imaging research. Since...

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy.

Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology...

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning.

Damage detection based on modal parameter changes has become popular in the last few decades. Nowadays, there are robust and reliable mathematical rel...

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infecte...

Improving the leak detection efficiency in water distribution networks using noise loggers.

Leak detection techniques are effective ways of controlling water leakage in real water distribution networks (WDNs). Nevertheless, developing detecti...

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the comp...

Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data.

Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved...

Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization.

Single-cell impedance flow cytometry (IFC) is emerging as a label-free and non-invasive method for characterizing the electrical properties and reveal...

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP).

Human T-cell Leukemia Virus type-1 (HTLV-1) is an oncovirus that may cause two main life-threatening diseases including a cancer type named Adult T-ce...

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA...