Machine learning for medical ultrasound: status, methods, and future opportunities.
Laura J. Brattain,Brian A. Telfer,Manish Dhyani,Manish Dhyani,Joseph R. Grajo,Anthony E. Samir +5 more
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TLDR
Leading machine learning approaches and research directions in US are reviewed, with an emphasis on recent ML advances, and an outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization is presented.Abstract:
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.read more
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High frequency ultrasound: a novel instrument to quantify granuloma burden in cutaneous sarcoidosis.
Megan H. Noe,Olaf Rodriguez,Laura A. Taylor,Laith R. Sultan,Chandra M. Sehgal,Susan M. Schultz,Joel M. Gelfand,Marc A. Judson,Misha Rosenbach +8 more
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Decision quality support in diagnostic breast ultrasound through artificial Intelligence
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TL;DR: ClearView cCAD algorithms are evaluated to increase overall performance and reduce the inter-operator variance on a set of imaged lesions and it is shown that through simple fusion schemes they are able to increase performance beyond that of either the cC AD system or the radiologist alone by all typically tracked quality metrics, and significantly reduce inter- operator variance.
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Weakly supervised learning of placental ultrasound images with residual networks
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Proceedings ArticleDOI
Identification of ovarian mass through ultrasound images using machine learning techniques
Hemita Pathak,Vrushali Kulkarni +1 more
TL;DR: Preliminary results depict that the proposed technique can be reliable for ovarian tumor classification as this system is fully automated, advantageous and cost-effective too.