Oncology/Hematology

Brain Cancer

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

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Radiomics in neuro-oncological clinical trials.

The development of clinical trials has led to substantial improvements in the prevention and treatme...

Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.

INTRODUCTION: Contouring organs at risk (OARs) is a time-intensive task that is a critical part of r...

Auto-segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning.

BACKGROUND: Routinely delineating of important skeletal growth centers is imperative to mitigate rad...

Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study.

Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, ...

Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.

According to the limited molecular information reflected by single spectroscopy, and the complementa...

An Intelligent Robot Detection System of Uncontrolled Radioactive Sources.

In recent years, radioactive sources have been widely used in various fields (e.g., nuclear industry...

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillf...

The three horizons model applied to medical science.

The three horizons model is a framework that helps manage an organization's innovation strategy. Thi...

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient fol...

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial r...

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

PURPOSE: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based...

A data augmentation method for fully automatic brain tumor segmentation.

Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatmen...

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly re...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Clinical diagnosis has become very significant in today's health system. The most serious disease a...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast c...

Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.

Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer wit...

Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning.

BACKGROUND AND PURPOSE: Changes of healthy appearing brain tissue after radiotherapy (RT) have been ...

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation betwee...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely unc...

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