AIMC Topic: Deep Learning

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Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance.

JAMA network open
IMPORTANCE: When evaluating surgeons in the operating room, experienced physicians must rely on live or recorded video to assess the surgeon's technical performance, an approach prone to subjectivity and error. Owing to the large number of surgical p...

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

JAMA network open
IMPORTANCE: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.

Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Physics in medicine and biology
Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensitie...

A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: EPID dosimetry in the Unity MR-Linac system allows for reconstruction of absolute dose distributions within the patient geometry. Dose reconstruction is accurate for the parts of the beam arriving at the EPID through the MRI central unattenu...

Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets.

Computer methods and programs in biomedicine
Background and objectivesAutomated segmentation and tracking of surgical instruments and catheters under X-ray fluoroscopy hold the potential for enhanced image guidance in catheter-based endovascular procedures. This article presents a novel method ...

Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

Gastroenterology
BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps b...

CNN-MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites.

Neural networks : the official journal of the International Neural Network Society
Increasing phishing sites today have posed great threats due to their terribly imperceptible hazard. They expect users to mistake them as legitimate ones so as to steal user information and properties without notice. The conventional way to mitigate ...

Multiple Discrimination and Pairwise CNN for view-based 3D object retrieval.

Neural networks : the official journal of the International Neural Network Society
With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep ...

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography; CT myocardium segmentation.

Medical physics
PURPOSE: Segmentation of left ventricular myocardium (LVM) in coronary computed tomography angiography (CCTA) is important for diagnosis of cardiovascular diseases. Due to poor image contrast and large variation in intensity and shapes, LVM segmentat...