Lymphoma

[MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has inc...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation su...

Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.

INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural l...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising tr...

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorec...

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review.

Neonatal jaundice is a common and potentially fatal health condition in neonates, especially in low ...

Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.

AIM: To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image qua...

Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.

Previous research has demonstrated that machine learning (ML) could not effectively decode passive o...

Cooperative Magnetic Interfacial Microrobot Couple for Versatile Non-Contact Biomedical Applications.

Magnetic interfacial microrobots are increasingly recognized as a promising approach for potential b...

Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers.

BACKGROUND: We evaluated the acceleration of a three-dimensional isotropic flow-independent magnetic...

Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Microscopic review of tissue sections is of foundational importance in pathology, yet the traditiona...

Non-destructive origin and ginsenoside analysis of American ginseng via NIR and deep learning.

American ginseng is widely in demand as a famous medicinal herb, but the production conditions affec...

Label-efficient sequential model-based weakly supervised intracranial hemorrhage segmentation in low-data non-contrast CT imaging.

BACKGROUND: In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-c...

Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.

BACKGROUND: Immunotherapy has revolutionized the treatment landscape for head and neck squamous cell...

Advancing tele-perception: a paradigm shift from traditional non-contact sensing to adaptive embodied artificial intelligence systems.

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.

PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the E...

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing....

Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.

Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia ...

Artificial Intelligence in Lymphoma Histopathology: Systematic Review.

BACKGROUND: Artificial intelligence (AI) shows considerable promise in the areas of lymphoma diagnos...

Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.

Inferring and understanding the underlying connectivity structure of a system solely from the observ...

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease managemen...