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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Interpretable Classification Models for Recidivism Prediction
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Anthony E. Samir,Manish Dhyani,Abhinav Vij,Atul K. Bhan,Elkan F. Halpern,Jorge Méndez-Navarro,Kathleen E. Corey,Raymond T. Chung +7 more
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Mirko D'Onofrio,Stefano Crosara,Riccardo De Robertis,Stefano Canestrini,Roberto Pozzi Mucelli +4 more
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A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound.
Karim Lekadir,Alfiia Galimzianova,Angels Betriu,Maria del Mar Vila,Laura Igual,Daniel L. Rubin,Elvira Fernández,Petia Radeva,Sandy Napel +8 more
TL;DR: This paper proposes to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.