Search Results - "van Ginneken, Bram"

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning by van Ginneken, Bram

“…Half a century ago, the term “computer-aided diagnosis” (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and…”
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iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network by Aresta, Guilherme, Jacobs, Colin, Araújo, Teresa, Cunha, António, Ramos, Isabel, van Ginneken, Bram, Campilho, Aurélio

“…We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is…”
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Automated measurement of fetal head circumference using 2D ultrasound images by van den Heuvel, Thomas L A, de Bruijn, Dagmar, de Korte, Chris L, Ginneken, Bram van

“…In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all…”
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Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box by Ciompi, Francesco, de Hoop, Bartjan, van Riel, Sarah J., Chung, Kaman, Scholten, Ernst Th, Oudkerk, Matthijs, de Jong, Pim A., Prokop, Mathias, Ginneken, Bram van

“…•Peri-fissural nodules (PFNs) have been proven to be bening nodules, for which no follow-up is needed.•Automatic classsification of PFNs would make lung cancer…”
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Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard by Bulten, Wouter, Bándi, Péter, Hoven, Jeffrey, Loo, Rob van de, Lotz, Johannes, Weiss, Nick, Laak, Jeroen van der, Ginneken, Bram van, Hulsbergen-van de Kaa, Christina, Litjens, Geert

“…Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important…”
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Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin by Ghafoorian, Mohsen, Karssemeijer, Nico, Heskes, Tom, Bergkamp, Mayra, Wissink, Joost, Obels, Jiri, Keizer, Karlijn, de Leeuw, Frank-Erik, Ginneken, Bram van, Marchiori, Elena, Platel, Bram

“…Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature…”
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Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management by van Riel, Sarah J., Jacobs, Colin, Scholten, Ernst Th, Wittenberg, Rianne, Winkler Wille, Mathilde M., de Hoop, Bartjan, Sprengers, Ralf, Mets, Onno M., Geurts, Bram, Prokop, Mathias, Schaefer-Prokop, Cornelia, van Ginneken, Bram

“…Objectives Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The…”
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Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval by Silva, Mario, Milanese, Gianluca, Sestini, Stefano, Sabia, Federica, Jacobs, Colin, van Ginneken, Bram, Prokop, Mathias, Schaefer-Prokop, Cornelia M., Marchianò, Alfonso, Sverzellati, Nicola, Pastorino, Ugo

“…Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of…”
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Intracerebral Haemorrhage Segmentation in Non-Contrast CT by Patel, Ajay, Schreuder, Floris H. B. M., Klijn, Catharina J. M., Prokop, Mathias, Ginneken, Bram van, Marquering, Henk A., Roos, Yvo B. W. E. M., Baharoglu, M. Irem, Meijer, Frederick J. A., Manniesing, Rashindra

“…A 3-dimensional (3D) convolutional neural network is presented for the segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in…”
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Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines by van Riel, Sarah J., Ciompi, Francesco, Jacobs, Colin, Winkler Wille, Mathilde M., Scholten, Ernst Th, Naqibullah, Matiullah, Lam, Stephen, Prokop, Mathias, Schaefer-Prokop, Cornelia, van Ginneken, Bram

“…Objectives To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from…”
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Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes by Abràmoff, Michael D, Niemeijer, Meindert, Suttorp-Schulten, Maria S.A, Viergever, Max A, Russell, Stephen R, van Ginneken, Bram

“…OBJECTIVE:--To evaluate the performance of a system for automated detection of diabetic retinopathy in digital retinal photographs, built from published…”
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Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique by Greenspan, Hayit, van Ginneken, Bram, Summers, Ronald M.

“…The papers in this special section focus on the technology and applications supported by deep learning. Deep learning is a growing trend in general data…”
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Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment by van Leeuwen, Kicky G., Meijer, Frederick J. A., Schalekamp, Steven, Rutten, Matthieu J. C. M., van Dijk, Ewoud J., van Ginneken, Bram, Govers, Tim M., de Rooij, Maarten

“…Background Limited evidence is available on the clinical impact of artificial intelligence (AI) in radiology. Early health technology assessment (HTA) is a…”
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Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images by Pinckaers, Hans, van Ginneken, Bram, Litjens, Geert

“…Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets…”
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Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans by Xie, Weiyi, Jacobs, Colin, Charbonnier, Jean-Paul, van Ginneken, Bram

“…Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural…”
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Normal Range of Emphysema and Air Trapping on CT in Young Men by METS, Onno M, HULST, Robert A. Van, JACOBS, Colin, GINNEKEN, Bram Van, DE JONG, Pim A

“…The purpose of our study was to assess the normal range of CT measures of emphysema and air trapping in young men with normal lung function. A cohort of 70…”
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Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing by Chlebus, Grzegorz, Schenk, Andrea, Moltz, Jan Hendrik, van Ginneken, Bram, Hahn, Horst Karl, Meine, Hans

“…Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and…”
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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises by Zhou, S. Kevin, Greenspan, Hayit, Davatzikos, Christos, Duncan, James S., Van Ginneken, Bram, Madabhushi, Anant, Prince, Jerry L., Rueckert, Daniel, Summers, Ronald M.

“…Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging…”
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Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms by Vanobberghen, Fiona, Keter, Alfred Kipyegon, Jacobs, Bart K M, Glass, Tracy R, Lynen, Lutgarde, Law, Irwin, Murphy, Keelin, van Ginneken, Bram, Ayakaka, Irene, van Heerden, Alastair, Maama, Llang, Reither, Klaus

“…Use of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if…”
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Deep learning for chest X-ray analysis: A survey by Çallı, Erdi, Sogancioglu, Ecem, van Ginneken, Bram, van Leeuwen, Kicky G., Murphy, Keelin

“…•The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of…”
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