Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

8,999 articles
Stay Ahead - Weekly Lymphoma research updates
Subscribe
Browse Categories
Showing 1345-1365 of 8,999 articles
Application of a Deep-Learning Technique to Non-Linear Images From Human Tissue Biopsies for Shedding New Light on Breast Cancer Diagnosis.

The development of label-free non-destructive techniques to be used as diagnostic tools in cancer re...

Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies.

PURPOSE: The registration of medical images often suffers from missing correspondences due to inter-...

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 ...

Versatile memristor for memory and neuromorphic computing.

The memristor is a promising candidate to implement high-density memory and neuromorphic computing. ...

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 ...

Non-radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports.

INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has t...

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...

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques.

INTRODUCTION: Heart disease is emerging as the single most critical cause of death worldwide and is ...

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients ...

On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 po...

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 cervi...

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 de...

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer.

Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non...

Subtype classification of malignant lymphoma using immunohistochemical staining pattern.

PURPOSE: For the image classification problem, the construction of appropriate training data is impo...

Incremental learning algorithm for large-scale semi-supervised ordinal regression.

As a special case of multi-classification, ordinal regression (also known as ordinal classification)...

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...

A non-invasive method for concurrent detection of early-stage women-specific cancers.

We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analys...

Open, Video- and Robot-Assisted Thoracoscopic Lobectomy for Stage II-IIIA Non-Small Cell Lung Cancer.

BACKGROUND: This study compares the short- and long-term outcomes of open vs robotic vs video-assist...

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 ...

Browse Categories