Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become a...
After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases i...
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized meth...
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging...
Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unpre...
Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current st...
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and th...
This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of sim...
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and ...
Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and ...
Artificial intelligence (AI) and machine learning (ML) have influenced medicine in myriad ways, and medical imaging is at the forefront of technologic...
The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates bo...
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data...
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, spee...
Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source o...
The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by l...
The large volume of data captured daily in healthcare institutions is opening new and great perspectives about the best ways to use it towards improvi...