AI Medical Compendium Journal:
International journal of computer assisted radiology and surgery

Showing 81 to 90 of 481 articles

Generative pretrained transformer-4, an artificial intelligence text predictive model, has a high capability for passing novel written radiology exam questions.

International journal of computer assisted radiology and surgery
PURPOSE: AI-image interpretation, through convolutional neural networks, shows increasing capability within radiology. These models have achieved impressive performance in specific tasks within controlled settings, but possess inherent limitations, s...

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are compl...

Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images.

International journal of computer assisted radiology and surgery
PURPOSE: A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical ins...

Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.

International journal of computer assisted radiology and surgery
PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. Conservative treatment is the preferred treatment for managing OCD, and early intervention significan...

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.

International journal of computer assisted radiology and surgery
PURPOSE: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to estab...

DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception.

International journal of computer assisted radiology and surgery
PURPOSE: Semantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse...

SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset.

International journal of computer assisted radiology and surgery
PURPOSE: We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural netwo...

Online advance respiration prediction model for percutaneous puncture robotics.

International journal of computer assisted radiology and surgery
PURPOSE: Surgical robots have significant research value and clinical significance in the field of percutaneous punctures. There have been numerous studies on ultrasound-guided percutaneous surgical robots; however, addressing the respiratory compens...

Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate and automatic segmentation of basal ganglia from magnetic resonance (MR) images is important for diagnosis and treatment of various brain disorders. However, the basal ganglia segmentation is a challenging task because of the class ...

High-quality semi-supervised anomaly detection with generative adversarial networks.

International journal of computer assisted radiology and surgery
PURPOSE: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is chal...