Oncology/Hematology

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

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Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.

AIM: This study aimed to develop highly precise radiomics and deep learning models to accurately det...

Agent-based approaches for biological modeling in oncology: A literature review.

CONTEXT: Computational modeling involves the use of computer simulations and models to study and und...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors befo...

The potential value of oral microbial signatures for prediction of oral squamous cell carcinoma based on machine learning algorithms.

OBJECTIVE: This study aimed to explore the potential predictive value of oral microbial signatures f...

ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier.

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhi...

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive pe...

Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.

Metabolomics generates complex data necessitating advanced computational methods for generating biol...

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of m...

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is close...

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning.

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful f...

Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features ...

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

BACKGROUND: This study aims to identify precise biomarkers for breast cancer to improve patient outc...

Automatic Skeleton Segmentation in CT Images Based on U-Net.

Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct cli...

An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation.

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ab...

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