Hematology

Lymphoma

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

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Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classificatio...

The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice.

Research investigating the nature and scope of developmental participation patterns leading to inter...

Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.

BACKGROUND: Completion axillary lymph node dissection is overtreatment for patients with sentinel ly...

A non-parametric effect-size measure capturing changes in central tendency and data distribution shape.

MOTIVATION: Calculating the magnitude of treatment effects or of differences between two groups is a...

Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics.

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroi...

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser pho...

Machine learning at the interface of structural health monitoring and non-destructive evaluation.

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objecti...

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact du...

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI.

PURPOSE: Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. Ho...

Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus.

Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly i...

Design and development of a non-contact robotic gripper for tissue manipulation in minimally invasive surgery.

This paper describes the design and testing of a gripper developed for handling of delicate and flex...

Democratizing AI: non-expert design of prediction tasks.

Non-experts have long made important contributions to machine learning (ML) by contributing training...

Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.

BACKGROUND AND AIMS: Colonoscopy is commonly performed for colorectal cancer screening in the United...

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achie...

Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health.

BACKGROUND: The oral microbiome is a complex assembly of microbial species, whose constituents can t...

Zwitterionic 3D-Printed Non-Immunogenic Stealth Microrobots.

Microrobots offer transformative solutions for non-invasive medical interventions due to their small...

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate...

ncRDeep: Non-coding RNA classification with convolutional neural network.

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involve...

Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.

PURPOSE: Deep learning-based whole-heart segmentation in coronary computed tomography angiography (C...

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