Hematology

Lymphoma

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

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Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives.

Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological...

Variant anatomy of non-recurrent laryngeal nerve: when and how should it be taught in surgical residency?

INTRODUCTION: While the performance of a thyroidectomy is generally associated with a low risk of in...

A Horizon Scan to Support Chemical Pollution-Related Policymaking for Sustainable and Climate-Resilient Economies.

While chemicals are vital to modern society through materials, agriculture, textiles, new technology...

Are ChatGPT and large language models "the answer" to bringing us closer to systematic review automation?

In this commentary, we discuss ChatGPT and our perspectives on its utility to systematic reviews (SR...

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Recent advances in single-cell sequencing technology have made it possible to measure multiple paire...

Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.

Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, hig...

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predi...

Performance-Based Robotic Training in Individuals with Subacute Stroke: Differences between Responders and Non-Responders.

The high variability of upper limb motor recovery with robotic training (RT) in subacute stroke unde...

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations a...

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics...

Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels.

PURPOSE: To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (Res...

Collaborative training of medical artificial intelligence models with non-uniform labels.

Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep l...

Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images.

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the two most common skin cancer and...

Non-equilibrium-Growing Aesthetic Ionic Skin for Fingertip-Like Strain-Undisturbed Tactile Sensation and Texture Recognition.

Humans use periodically ridged fingertips to precisely perceive the characteristics of objects via i...

Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model.

BACKGROUND: Access to oral healthcare is not uniform globally, particularly in rural areas with limi...

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection.

BACKGROUND: This study investigated the geographical origin classification of green coffee beans fro...

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.

BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data colle...

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