Oncology/Hematology

Brain Cancer

Latest AI and machine learning research in brain cancer for healthcare professionals.

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Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.

Preoperative classification of brain tumors is critical to developing personalized treatment plans, ...

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targ...

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnosti...

Low-dose CT reconstruction using cross-domain deep learning with domain transfer module.

. X-ray computed tomography employing low-dose x-ray source is actively researched to reduce radiati...

Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography.

PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out...

CDAF-Net: A Contextual Contrast Detail Attention Feature Fusion Network for Low-Dose CT Denoising.

Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal...

Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.

PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-ba...

Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI.

PURPOSE: To develop and compare machine learning models for distinguishing low and high grade mening...

Advances of artificial intelligence in clinical application and scientific research of neuro-oncology: Current knowledge and future perspectives.

Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both pri...

ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies.

Gene selection is crucial for cancer classification using microarray data. In the interests of impro...

Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.

OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (D...

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) presents a promising approach to balancing high image quali...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Health is fundamental to human well-being, with brain health particularly critical for cognitive fun...

Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in pr...

Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.

Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especi...

Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting ...

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