Lymphoma

High angular resolution diffusion imaging of neurodevelopment in children through data creation with deep learning.

High-angular resolution diffusion imaging (HARDI) is an advanced method for characterizing brain microstructure and function. However, HARDI is time-consuming limiting real-world applications, especially in children. We aimed to address the challenge by creating non-acquired HARDI data through deep learning and testing utility in neurodevelopment. Brain diffusion magnetic resonance imaging from 95...

Artificial intelligence driven multiphysics analysis of Casson bioconvective ternary nanofluid flow over an inclined needle.

This study investigates the bio-convective flow of ternary Casson nanofluid across inclined moving thin needle embedded in a porous medium, considering Arrhenius activation energy and non-uniform heat generation/absorption. Such models are important in biomedical engineering, thermal energy systems, and advanced cooling technologies, where enhanced heat and mass transfer characteristics are requir...

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

OBJECTIVE: To develop and validate an interpretable machine learning model based on multicenter T1-weighted MRI radiomics data for the three-way class...

Integrating machine learning with histopathological, immunohistochemical, and PET parameters in the study of invasive breast carcinoma, no special type.

BACKGROUND: This study investigates the relationship between histopathological (HP) features, immunohistochemical (IHC) markers, 18F- FDG PET/CT param...

Predicting band gap from chemical composition: a comparative study of machine learning and deep learning models.

Predicting the band gap of inorganic semiconductors is crucial for designing materials used in electronic and optoelectronic applications. This study ...

Sézary Syndrome: Survival Trends, Racial Disparities, and Limited Prognostic Value of Routine Registry Variables.

INTRODUCTION: Sézary syndrome (SS) is a rare leukemic cutaneous T-cell lymphoma with limited contemporary population-level outcome data. METHODS: Usin...

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Radiology report generation is an important application of artificial intelligence (AI), as the interpretation of medical images and the production of...

Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review.

BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and un...

CeraMIRScan: Mid-infrared OCT Scan Dataset for Ceramic Quality Assessment.

Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabili...

Multimodal PET/CT-based PD-L1 status prediction in lung cancer via semi-supervised and unsupervised deep learning.

Deep learning (DL) techniques have been applied in lung cancer screening, assessing drug effectiveness, and enhancing prognosis prediction. Within thi...

Efficient training of machine learning models using non-converged iterates in nuclear reactor simulations.

Machine learning (ML) algorithms exhibit promising potential for enhancing safety, improving predictive accuracy, and streamlining nuclear reactor sys...

Using machine learning algorithms based on laboratory indicators to establish a diagnostic model for lung cancer.

BACKGROUND: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Current diagnostic strategies rely primarily on imagi...

A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.

BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face l...

Kernel PCA for out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation.

Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disp...

An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) me...

Path beyond the blind end-unravel the imaging spectrum of appendiceal pathologies.

The appendix is involved in a diverse spectrum of inflammatory, infectious, benign, and malignant conditions that extend far beyond acute appendicitis...

Digital image analysis coupled with artificial neural networks for simultaneous prediction of biomass and pigments in microalgal cultivation.

Real-time, non-destructive monitoring of multiple physiological parameters in microalgal cultures remains a significant analytical challenge, as conve...

Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis.

BACKGROUND: Axillary lymph node metastasis (ALNM) is a critical prognostic factor in breast cancer. While sentinel lymph node biopsy remains the gold ...

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Laryngeal...