Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Showing 2311-2331 of 15,297 articles
A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with v...

AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.

BACKGROUND: Non-muscle-invasive Bladder Cancer (NMIBC) is notorious for its high recurrence rate of ...

Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.

OBJECTIVE: To use bibliometric methods to analyze the prospects and development trends of artificial...

Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence a...

Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management.

OBJECTIVES: Patients with nasopharyngeal carcinoma (NPC) are prone to malnutrition, which leads to d...

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach...

Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal grow...

Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.

This study focuses on the use of machine learning (ML) models to predict the biodistribution of nano...

Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique ...

Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients.

Objective Endometrial lesions are a frequent complication following breast cancer, and current diagn...

Feasibility of reconstructingpatient 3D dose distributions from 2D EPID image data using convolutional neural networks.

. The primary purpose of this work is to demonstrate the feasibility of a deep convolutional neural ...

Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical t...

Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.

OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence ca...

Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.

OBJECTIVE: To develop a machine learning-based clinical and/or radiomics model for predicting the pr...

The global trends and distribution in tumor-infiltrating lymphocytes over the past 49 years: bibliometric and visualized analysis.

BACKGROUND: The body of research on tumor-infiltrating lymphocytes (TILs) is expanding rapidly; yet,...

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) ...

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