Oncology/Hematology

Brain Cancer

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

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Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types.

The growing burden of cancer and recent surge in healthcare data availability call for new ways of a...

Towards Optical Biopsy in Glioma Surgery.

Currently, the focus of intraoperative imaging in brain tumor surgery is beginning to shift to optic...

Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients.

PURPOSE: This study aims to create a deep learning (DL) model capable of accurately delineating the ...

[Urban Ozone Driving Factors Based on Explainable Machine Learning].

Sixteen sites in the coastal city of Qingdao, including eight national control sites, seven provinci...

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.

This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritu...

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells...

New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods.

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute ...

Potential of artificial intelligence for radiation dose reduction in computed tomography -A scoping review.

INTRODUCTION: Artificial intelligence (AI) is now transforming medical imaging, with extensive ramif...

Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis.

BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often...

Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI.

Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier lea...

Development of an automated photolysis rates prediction system based on machine learning.

Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations,...

A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.

BACKGROUND AND PURPOSE: We aimed to develop a physics-informed deep learning model for beam dose pre...

Revolutionizing Brain Tumor Detection Using Explainable AI in MRI Images.

Due to the complex structure of the brain, variations in tumor shapes and sizes, and the resemblance...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cel...

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for...

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