Oncology/Hematology

Brain Cancer

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

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Ensemble based machine learning approach for prediction of glioma and multi-grade classification.

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the ...

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce.

OBJECTIVE: Radiation oncology is a continually evolving speciality. With the development of new imag...

DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibr...

The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.

This retrospective study has been conducted to validate the performance of deep learning-based survi...

Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland.

INTRODUCTION: Implementation of Artificial Intelligence (AI) into medical imaging is much debated. D...

A deep learning-based dual-omics prediction model for radiation pneumonitis.

PURPOSE: Radiation pneumonitis (RP) is the main source of toxicity in thoracic radiotherapy. This st...

Artificial Intelligence in Radiation Therapy.

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy....

Deep learning method for prediction of patient-specific dose distribution in breast cancer.

BACKGROUND: Patient-specific dose prediction improves the efficiency and quality of radiation treatm...

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) ...

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanc...

Deep learning for segmentation in radiation therapy planning: a review.

Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influe...

Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.

A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from gliob...

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with featu...

Aggregation-and-Attention Network for brain tumor segmentation.

BACKGROUND: Glioma is a malignant brain tumor; its location is complex and is difficult to remove su...

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission comp...

Unsupervised water scene dehazing network using multiple scattering model.

In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are ...

Artificial intelligence in medical imaging: implications for patient radiation safety.

Artificial intelligence, including deep learning, is currently revolutionising the field of medical ...

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Cons...

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