Since DL has been so successful, it is now essential for resolving medical issues. DL and derivative methodologies are important because early diagnosis is essential for human survival. In this study, the bibliometric summary of the topic of interest is examined, and the size of the huge data ...
Four years later,deep learningremains the most promising and widely used ML technique for radiology in particular and disease detection in general. It comes as no surprise as diagnostic imagingprevailsin clinical diagnosis andimage recognitionis a natural fit for deep learning algorithms. That’s what...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020....
Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts. However, it has a significant problem that these models are “black-box” structures, which means they are opaque, non-intuitive, and difficult for people ...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted
Healthcare applications of deep learning range from one-dimensional biosignal analysis [16] and the prediction of medical events, e.g. seizures [17] and cardiac arrests [18], to computer-aided detection [19] and diagnosis [20] supporting clinical decision making and survival analysis [21], ...
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of dee...
Explainable AI for medical imaging: Deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI. In Proceedings of the SPIE Medical Imaging 2020: Computer-Aided Diagnosis; International Society for Optics and Photonics: Bellingham, WA, USA, 2020; Volume 11314, p. 1131...
In contrast, nonarteritic anterior ischemic optic neuropathy (NAION) is a clinical diagnosis for which no acute intervention has been shown to reduce risk of fellow eye involvement over the longer term. Differentiating between these 2 anterior optic neuropathies in the acute setting ...
vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are ...