Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated d...
Recent advances in deep learning methods have redefined the state-of-the-art for many medical imaging applications, surpassing previous approaches and sometimes even competing with human judgment in several tasks. Those models, however, when trained ...
UNLABELLED: Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases BACKGROUND: In the last decades, new optimization methods based on the nature's intelligence were...
IEEE journal of biomedical and health informatics
Feb 28, 2019
The figures found in biomedical literature are a vital part of biomedical research, education, and clinical decision. The multitude of their modalities and the lack of corresponding metadata constitute search and information, retrieval a difficult ta...
The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains c...
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but al...
Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near futu...
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