The size of the training database is a function of model complexity rather than a characteristic of machine learning methods.doi:10.1016/j.media.2016.06.002CriminisiA.Elsevier B.V.Medical Image AnalysisCriminisi, A. Machine learning for medical images analysis. Medical image analysis 33 (2016) 91...
Machine learning, the cornerstone of today’s artificial intelligence (AI) revolution, brings new promises to clinical practice with medical images1,2,3. For example, to diagnose various conditions from medical images, machine learning has been shown to perform on par with medical experts4. Softwar...
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the ...
Thus new strategies for imaging-based CAD and therapies of diseases have to be developed. In recent years, machine learning became one of the major tools of medical image analysis in various CAD applications. Prior knowledge being learnt from character- istic examples provided by medical experts ...
Ad Asmedical technologyimproves, however, it also significantly increases the workload of the already overworked medical staff. It can create information stress that can cause delays and errors. More efficient methods forimage processingwould therefore be a much-needed improvement. ...
The MLMI 2018 proceedings deal with machine learning in medical imaging and focus on major trends and challenges in the area, including computer-assisted diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotat
Table 1. Different learning frameworks and strategies, together with some of the most popular algorithms or techniques that are used for each of them, as well as a few examples of common applications in the field of medical imaging. The table is divided in three parts: the basic learning fra...
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the
16. Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning for electronic health records. arXiv. January 24, 2018 (http://arxiv.org/abs/1801.07860). 17. Escobar GJ, Turk BJ, Ragins A, et al. Piloting electronic medical record-based early detection of inpatient deterio...
Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilisi