Search Results - "Armato III, Samuel G."

Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers by Zinovev, Dmitriy, Raicu, Daniela, Furst, Jacob, Armato III, Samuel G.

“…This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database…”
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Harmonization of radiomic feature variability resulting from differences in CT image acquisition and reconstruction: assessment in a cadaveric liver by Foy, Joseph J, Al-Hallaq, Hania A, Grekoski, Vincent, Tran, Tri, Guruvadoo, Kharina, Armato Iii, Samuel G, Sensakovic, William F

“…Studies investigating the effects of computed tomography (CT) image acquisition and reconstruction parameters have mostly been limited to non-human phantoms to…”
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Lymphatic vessel density is not associated with lymph node metastasis in non-small cell lung carcinoma by Faoro, Leonardo, Hutto, Joann Y, Salgia, Ravi, El-Zayaty, Shady A, Ferguson, Mark K, Cheney, Richard T, Reid, Mary E, Armato, 3rd, Samuel G, Krausz, Thomas, Husain, Aliya N

“…Angiogenesis is essential for tumors to grow and metastasize. Lymphatic metastasis is also an important means of tumor spread. In non-small cell lung…”
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AI in medical imaging grand challenges: translation from competition to research benefit and patient care by Armato, 3rd, Samuel G, Drukker, Karen, Hadjiiski, Lubomir

“…Artificial intelligence (AI), in one form or another, has been a part of medical imaging for decades. The recent evolution of AI into approaches such as deep…”
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Best Practices for Artificial Intelligence and Machine Learning for Computer-Aided Diagnosis in Medical Imaging by Vergara, Daniel, Armato, Samuel G., Hadjiiski, Lubomir, Drukker, Karen

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Imaging in pleural Mesothelioma: A review of the 16th International Conference of the International Mesothelioma Interest Group by Armato, Samuel G., Katz, Sharyn I., Frauenfelder, Thomas, Jayasekera, Geeshath, Catino, Annamaria, Blyth, Kevin G., Theodoro, Taylla, Rousset, Pascal, Nackaerts, Kristiaan, Opitz, Isabelle

“…•Harmonizing imaging practices will allow for well-informed decisions in oncology.•Imaging technology impacts surgical approaches and will shape future…”
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Initial experience with AAPM's new publisher by Armato, Samuel G., Williamson, Jeffrey F.

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Multicenter, Double-Blind, Placebo-Controlled, Randomized Phase II Trial of Gemcitabine/Cisplatin Plus Bevacizumab or Placebo in Patients With Malignant Mesothelioma by KINDLER, Hedy L, KARRISON, Theodore G, ALBAIN, Kathy S, TABER, David A, ARMATO, Samuel G, VOGELZANG, Nicholas J, CHEN, Helen X, STADLER, Walter M, VOKES, Everett E, GANDARA, David R, LU, Charles, KRUG, Lee M, STEVENSON, James P, JÄNNE, Pasi A, QUINN, David I, KOCZYWAS, Marianna N, BRAHMER, Julie R

“…Gemcitabine plus cisplatin is active in malignant mesothelioma (MM), although single-arm phase II trials have reported variable outcomes. Vascular endothelial…”
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Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group by Katz, Sharyn I, Straus, Christopher M, Roshkovan, Leonid, Blyth, Kevin G, Frauenfelder, Thomas, Gill, Ritu R, Lalezari, Ferry, Erasmus, Jeremy, Nowak, Anna K, Gerbaudo, Victor H, Francis, Roslyn J, Armato, 3rd, Samuel G

“…Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and…”
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Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype by Kremer, Linnea E, Chapman, Arlene B, Armato, 3rd, Samuel G

“…Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma…”
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Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors by Shenouda, Mena, Flerlage, Isabella, Kaveti, Aditi, Giger, Maryellen L, Armato, 3rd, Samuel G

“…The purpose is to assess the performance of a pre-trained deep learning model in the task of classifying between coronavirus disease (COVID)-positive and…”
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Automated lung segmentation for thoracic CT impact on computer-aided diagnosis by Armato, 3rd, Samuel G, Sensakovic, William F

“…Automated lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic (CAD) methods. A core…”
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Implementation of a high-resolution, high-contrast magnetic resonance imaging protocol with extended delayed phases for peritoneal mesothelioma by Medved, Milica, Witmer, Hunter D D, Dhiman, Ankit, Berger, Yaniv, Sherman, Scott K, Hindi, Enal S, Armato, 3rd, Samuel G, Reiser, Ingrid S, Oto, Aytekin, Engelmann, Roger M, Kindler, Hedy L, Oren, Nisa C, Harmath, Carla B, Turaga, Kiran K

“…Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide…”
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The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements by Reeves, Anthony P, Biancardi, Alberto M, Apanasovich, Tatiyana V, Meyer, Charles R, MacMahon, Heber, van Beek, Edwin J R, Kazerooni, Ella A, Yankelevitz, David, McNitt-Gray, Michael F, McLennan, Geoffrey, Armato, 3rd, Samuel G, Henschke, Claudia I, Aberle, Denise R, Croft, Barbara Y, Clarke, Laurence P

“…The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule…”
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Computer-assisted staging of chronic rhinosinusitis correlates with symptoms by Garneau, Jonathan, Ramirez, Michael, Armato III, Samuel G., Sensakovic, William F., Ford, Megan K., Poon, Colin S., Ginat, Daniel T., Starkey, Adam, Baroody, Fuad M., Pinto, Jayant M.

“…Background The Lund‐Mackay (LM) staging system for chronic rhinosinusitis (CRS) does not correlate with clinical parameters, likely due to its coarse scale. We…”
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Evaluation of lung MDCT nodule annotation across radiologists and methods by Meyer, Charles R, Johnson, Timothy D, McLennan, Geoffrey, Aberle, Denise R, Kazerooni, Ella A, Macmahon, Heber, Mullan, Brian F, Yankelevitz, David F, van Beek, Edwin J R, Armato, 3rd, Samuel G, McNitt-Gray, Michael F, Reeves, Anthony P, Gur, David, Henschke, Claudia I, Hoffman, Eric A, Bland, Peyton H, Laderach, Gary, Pais, Richie, Qing, David, Piker, Chris, Guo, Junfeng, Starkey, Adam, Max, Daniel, Croft, Barbara Y, Clarke, Laurence P

“…Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of…”
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The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth" by Armato, 3rd, Samuel G, Roberts, Rachael Y, McNitt-Gray, Michael F, Meyer, Charles R, Reeves, Anthony P, McLennan, Geoffrey, Engelmann, Roger M, Bland, Peyton H, Aberle, Denise R, Kazerooni, Ella A, MacMahon, Heber, van Beek, Edwin J R, Yankelevitz, David, Croft, Barbara Y, Clarke, Laurence P

“…Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training,…”
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Variability in Mesothelioma Tumor Response Classification by Armato, Samuel G., III, Ogarek, Joseph L, Starkey, Adam, Vogelzang, Nicholas J, Kindler, Hedy L, Kocherginsky, Masha, MacMahon, Heber

“…The objective of our study was to evaluate observer variability in the measurement of temporal change in mesothelioma tumor thickness and in the resulting…”
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Discrete-space versus continuous-space lesion boundary and area definitions by Sensakovic, William F., Starkey, Adam, Roberts, Rachael Y., Armato, Samuel G.

“…Measurement of the size of anatomic regions of interest in medical images is used to diagnose disease, track growth, and evaluate response to therapy. The…”
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Anniversary Paper: Image processing and manipulation through the pages of Medical Physics by Armato, Samuel G., van Ginneken, Bram

“…The language of radiology has gradually evolved from “the film” (the foundation of radiology since Wilhelm Roentgen’s 1895 discovery of x-rays) to “the image,”…”
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