Oncology/Hematology

Brain Cancer

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

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Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correct...

Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian can...

Deep learning based analysis of microstructured materials for thermal radiation control.

Microstructured materials that can selectively control the optical properties are crucial for the de...

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) me...

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximat...

Design of Nuclear Radiation Monitoring System in Floor Exploration Based on Deep Learning.

Nuclear radiation environmental monitoring has become an important issue in floor surveys. From the ...

AI-based optimization for US-guided radiation therapy of the prostate.

OBJECTIVES: Fast volumetric ultrasound presents an interesting modality for continuous and real-time...

End-to-end deep learning for interior tomography with low-dose x-ray CT.

There are several x-ray computed tomography (CT) scanning strategies used to reduce radiation dose, ...

Reversing radiation-induced immunosuppression using a new therapeutic modality.

Radiation-induced immune suppression poses significant health challenges for millions of patients un...

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for ...

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.

This study presents a methodology based on the dual-mode gamma densitometry technique in combination...

Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review.

OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clin...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury....

MBP-11901 Inhibits Tumor Growth of Hepatocellular Carcinoma through Multitargeted Inhibition of Receptor Tyrosine Kinases.

Hepatocellular carcinomas (HCCs) are aggressive tumors with a poor prognosis. Approved first-line tr...

A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.

Accurate glioma subtype classification is critical for the treatment management of patients with bra...

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX...

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph.

Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to under...

The role of artificial intelligence in paediatric neuroradiology.

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncol...

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