Radiology

AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become on...

Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks.

BACKGROUND: An intracranial aneurysm is a cerebrovascular disorder that can result in various diseas...

Predicting O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neu...

Brain pathology identification using computer aided diagnostic tool: A systematic review.

Computer aided diagnostic (CAD) has become a significant tool in expanding patient quality-of-life b...

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the d...

Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography.

In patients with obstructive coronary artery disease, the functional significance of a coronary arte...

Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late...

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study.

BACKGROUND: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenecto...

Multifunctional Nanorobot System for Active Therapeutic Delivery and Synergistic Chemo-photothermal Therapy.

Nanorobots are safe and exhibit powerful functionalities, including delivery, therapy, and diagnosis...

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures.

The difficulty of obtaining annotations to build training databases still slows down the adoption of...

A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

PURPOSE: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a ...

SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder.

White matter hyperintensities (WMHs) of presumed vascular origin are frequently observed in magnetic...

Patients' views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire.

OBJECTIVES: The patients' view on the implementation of artificial intelligence (AI) in radiology is...

New technological advancements for interventional oncology.

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

OBJECTIVE: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field ...

A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features.

In digital mammography, which is used for the early detection of breast tumors, oversight may occur ...

Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images.

Computer-aided segmentation of thyroid nodules in ultrasound imaging could assist in their accurate ...

A neural network approach to segment brain blood vessels in digital subtraction angiography.

BACKGROUND AND OBJECTIVE: Cerebrovascular diseases (CVDs) affect a large number of patients and ofte...

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguis...

Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.

BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of...

Deep Learning for Quantitative Cardiac MRI.

The recent advancement of deep learning techniques has profoundly impacted research on quantitative...