Search Results - "Way, Ted W."

Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours by Way, Ted W., Hadjiiski, Lubomir M., Sahiner, Berkman, Chan, Heang-Ping, Cascade, Philip N., Kazerooni, Ella A., Bogot, Naama, Zhou, Chuan

“…We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was…”
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Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study by Way, Ted W, Chan, Heang-Ping, Goodsitt, Mitchell M, Sahiner, Berkman, Hadjiiski, Lubomir M, Zhou, Chuan, Chughtai, Aamer

“…The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of…”
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Quantitative CT of lung nodules: Dependence of calibration on patient body size, anatomic region, and calibration nodule size for single- and dual-energy techniques by Goodsitt, Mitchell M., Chan, Heang-Ping, Way, Ted W., Schipper, Mathew J., Larson, Sandra C., Christodoulou, Emmanuel G.

“…Calcium concentration may be a useful feature for distinguishing benign from malignant lung nodules in computer-aided diagnosis. The calcium concentration can…”
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Computer-aided diagnosis of pulmonary nodules on CT scans: Improvement of classification performance with nodule surface features by Way, Ted W., Sahiner, Berkman, Chan, Heang-Ping, Hadjiiski, Lubomir, Cascade, Philip N., Chughtai, Aamer, Bogot, Naama, Kazerooni, Ella

“…The purpose of this work is to develop a computer-aided diagnosis (CAD) system to differentiate malignant and benign lung nodules on CT scans. A fully…”
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FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing by Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Khan, Suffian, Kreis, Benjamin, Lee, Brian, Liu, Mia, Lončar, Vladimir, Ngadiuba, Jennifer, Pedro, Kevin, Perez, Brandon, Pierini, Maurizio, Rankin, Dylan, Tran, Nhan, Trahms, Matthew, Tsaris, Aristeidis, Versteeg, Colin, Way, Ted W., Werran, Dustin, Wu, Zhenbin

“…Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous…”
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Effect of finite sample size on feature selection and classification: A simulation study by Way, Ted W., Sahiner, Berkman, Hadjiiski, Lubomir M., Chan, Heang-Ping

“…Purpose: The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an…”
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Accuracy of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners by Goodsitt, Mitchell M., Chan, Heang-Ping, Way, Ted W., Larson, Sandra C., Christodoulou, Emmanuel G., Kim, Jeomsoon

“…A study was performed to determine the accuracies and reproducibilities of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners. The…”
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Quantitative CT of lung nodules: Dependence of calibrationon patient body size, anatomic region, and calibration nodule sizefor single- and dual-energy techniques by Goodsitt, Mitchell M., Chan, Heang-Ping, Way, Ted W., Schipper, Mathew J., Larson, Sandra C., Christodoulou, Emmanuel G.

“…Calcium concentration may be a useful feature for distinguishing benign from malignant lung nodules in computer-aided diagnosis. The calcium concentration can…”
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Accuracy of the CT numbers of simulated lung nodules imagedwith multi-detector CT scanners by Goodsitt, Mitchell M., Chan, Heang-Ping, Way, Ted W., Larson, Sandra C., Christodoulou, Emmanuel G., Kim, Jeomsoon

“…A study was performed to determine the accuracies and reproducibilities of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners. The…”
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Accuracy of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners: Accuracy of CT numbers of simulated lung nodules by Goodsitt, Mitchell M., Chan, Heang-Ping, Way, Ted W., Larson, Sandra C., Christodoulou, Emmanuel G., Kim, Jeomsoon

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FPGA-accelerated machine learning inference as a service for particle physics computing by Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Khan, Suffian, Kreis, Benjamin, Lee, Brian, Liu, Mia, Lončar, Vladimir, Ngadiuba, Jennifer, Pedro, Kevin, Perez, Brandon, Pierini, Maurizio, Rankin, Dylan, Tran, Nhan, Trahms, Matthew, Tsaris, Aristeidis, Versteeg, Colin, Way, Ted W, Werran, Dustin, Wu, Zhenbin

“…Comput Softw Big Sci (2019) 3: 13 New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate…”
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