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Showing 1 to 12 of 22 entries
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Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.

IEEE transactions on bio-medical engineering

Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski M, Marks JR, King LM, Maley C, Hwang ES, Lo JY.
PMID: 34788216
IEEE Trans Biomed Eng. 2021 Nov 17; doi: 10.1109/TBME.2021.3126281. Epub 2021 Nov 17.

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised...

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Hou R, Peng Y, Grimm LJ, et al. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE Trans Biomed Eng. 2021;doi: 10.1109/TBME.2021.3126281.
Hou, R., Peng, Y., Grimm, L. J., Ren, Y., Mazurowski, M., Marks, J. R., King, L. M., Maley, C., Hwang, E. S., & Lo, J. Y. (2021). Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE transactions on bio-medical engineering, . https://doi.org/10.1109/TBME.2021.3126281
Hou, Rui, et al. "Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases." IEEE transactions on bio-medical engineering vol. (2021). doi: https://doi.org/10.1109/TBME.2021.3126281
Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski M, Marks JR, King LM, Maley C, Hwang ES, Lo JY. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE Trans Biomed Eng. 2021 Nov 17; doi: 10.1109/TBME.2021.3126281. Epub 2021 Nov 17. PMID: 34788216.
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Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases.

The Science of the total environment

Li L, Mazurowski L, Dewan A, Carine M, Haak L, Guarin TC, Dastjerdi NG, Gerrity D, Mentzer C, Pagilla KR.
PMID: 35016937
Sci Total Environ. 2022 Jan 08;152958. doi: 10.1016/j.scitotenv.2022.152958. Epub 2022 Jan 08.

In this study, wastewater-based surveillance was carried out to establish the correlation between SARS-CoV-2 viral RNA concentrations in wastewater and the incidence of corona virus disease 2019 (COVID-19) from clinical testing. The influent wastewater of three major water reclamation...

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Li L, Mazurowski L, Dewan A, et al. Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. Sci Total Environ. 2022;152958doi: 10.1016/j.scitotenv.2022.152958.
Li, L., Mazurowski, L., Dewan, A., Carine, M., Haak, L., Guarin, T. C., Dastjerdi, N. G., Gerrity, D., Mentzer, C., & Pagilla, K. R. (2022). Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. The Science of the total environment, 152958. https://doi.org/10.1016/j.scitotenv.2022.152958
Li, Lin, et al. "Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases." The Science of the total environment vol. (2022): 152958. doi: https://doi.org/10.1016/j.scitotenv.2022.152958
Li L, Mazurowski L, Dewan A, Carine M, Haak L, Guarin TC, Dastjerdi NG, Gerrity D, Mentzer C, Pagilla KR. Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. Sci Total Environ. 2022 Jan 08;152958. doi: 10.1016/j.scitotenv.2022.152958. Epub 2022 Jan 08. PMID: 35016937; PMCID: PMC8743272.
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Enhancing the tuning range of a single resonant band long period grating while maintaining the resonant peak depth by using an optimized high index indium tin oxide overlay.

Applied optics

Lee J, Chen Q, Zhang Q, Reichard K, Ditto D, Mazurowski J, Hackert M, Yin S.
PMID: 17906728
Appl Opt. 2007 Oct 01;46(28):6984-9. doi: 10.1364/ao.46.006984.

We report laboratory test results of a long period grating (LPG) that can maintain a constant resonant peak depth over an enhanced tuning range when it is coated with an indium tin oxide (ITO) electrode that has optimized thickness...

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Lee J, Chen Q, Zhang Q, et al. Enhancing the tuning range of a single resonant band long period grating while maintaining the resonant peak depth by using an optimized high index indium tin oxide overlay. Appl Opt. 2007;46(28):6984-9doi: 10.1364/ao.46.006984.
Lee, J., Chen, Q., Zhang, Q., Reichard, K., Ditto, D., Mazurowski, J., Hackert, M., & Yin, S. (2007). Enhancing the tuning range of a single resonant band long period grating while maintaining the resonant peak depth by using an optimized high index indium tin oxide overlay. Applied optics, 46(28), 6984-9. https://doi.org/10.1364/ao.46.006984
Lee, Jonathan, et al. "Enhancing the tuning range of a single resonant band long period grating while maintaining the resonant peak depth by using an optimized high index indium tin oxide overlay." Applied optics vol. 46,28 (2007): 6984-9. doi: https://doi.org/10.1364/ao.46.006984
Lee J, Chen Q, Zhang Q, Reichard K, Ditto D, Mazurowski J, Hackert M, Yin S. Enhancing the tuning range of a single resonant band long period grating while maintaining the resonant peak depth by using an optimized high index indium tin oxide overlay. Appl Opt. 2007 Oct 01;46(28):6984-9. doi: 10.1364/ao.46.006984. PMID: 17906728.
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A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

JAMA network open

Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, Lo JY, Mazurowski MA.
PMID: 34398205
JAMA Netw Open. 2021 Aug 02;4(8):e2119100. doi: 10.1001/jamanetworkopen.2021.19100.

IMPORTANCE: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the development and evaluation of...

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Buda M, Saha A, Walsh R, et al. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Netw Open. 2021;4(8):e2119100doi: 10.1001/jamanetworkopen.2021.19100.
Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Swiecicki, A., Lo, J. Y., & Mazurowski, M. A. (2021). A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA network open, 4(8), e2119100. https://doi.org/10.1001/jamanetworkopen.2021.19100
Buda, Mateusz, et al. "A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images." JAMA network open vol. 4,8 (2021): e2119100. doi: https://doi.org/10.1001/jamanetworkopen.2021.19100
Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, Lo JY, Mazurowski MA. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Netw Open. 2021 Aug 02;4(8):e2119100. doi: 10.1001/jamanetworkopen.2021.19100. PMID: 34398205; PMCID: PMC8369362.
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Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Radiology

Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, Lynch T, van Oirsouw M, Rogers K, Stone N, Wallis M, Teuwen J, Wesseling J, Hwang ES, Lo JY.
PMID: 34981975
Radiology. 2022 Jan 04;210407. doi: 10.1148/radiol.210407. Epub 2022 Jan 04.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility...

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Hou R, Grimm LJ, Mazurowski MA, et al. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology. 2022;210407doi: 10.1148/radiol.210407.
Hou, R., Grimm, L. J., Mazurowski, M. A., Marks, J. R., King, L. M., Maley, C. C., Lynch, T., van Oirsouw, M., Rogers, K., Stone, N., Wallis, M., Teuwen, J., Wesseling, J., Hwang, E. S., & Lo, J. Y. (2022). Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology, 210407. https://doi.org/10.1148/radiol.210407
Hou, Rui, et al. "Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features." Radiology vol. (2022): 210407. doi: https://doi.org/10.1148/radiol.210407
Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, Lynch T, van Oirsouw M, Rogers K, Stone N, Wallis M, Teuwen J, Wesseling J, Hwang ES, Lo JY. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology. 2022 Jan 04;210407. doi: 10.1148/radiol.210407. Epub 2022 Jan 04. PMID: 34981975.
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Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases.

The Science of the total environment

Li L, Mazurowski L, Dewan A, Carine M, Haak L, Guarin TC, Dastjerdi NG, Gerrity D, Mentzer C, Pagilla KR.
PMID: 35016937
Sci Total Environ. 2022 Jan 08;152958. doi: 10.1016/j.scitotenv.2022.152958. Epub 2022 Jan 08.

In this study, wastewater-based surveillance was carried out to establish the correlation between SARS-CoV-2 viral RNA concentrations in wastewater and the incidence of corona virus disease 2019 (COVID-19) from clinical testing. The influent wastewater of three major water reclamation...

Cite
Li L, Mazurowski L, Dewan A, et al. Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. Sci Total Environ. 2022;152958doi: 10.1016/j.scitotenv.2022.152958.
Li, L., Mazurowski, L., Dewan, A., Carine, M., Haak, L., Guarin, T. C., Dastjerdi, N. G., Gerrity, D., Mentzer, C., & Pagilla, K. R. (2022). Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. The Science of the total environment, 152958. https://doi.org/10.1016/j.scitotenv.2022.152958
Li, Lin, et al. "Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases." The Science of the total environment vol. (2022): 152958. doi: https://doi.org/10.1016/j.scitotenv.2022.152958
Li L, Mazurowski L, Dewan A, Carine M, Haak L, Guarin TC, Dastjerdi NG, Gerrity D, Mentzer C, Pagilla KR. Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases. Sci Total Environ. 2022 Jan 08;152958. doi: 10.1016/j.scitotenv.2022.152958. Epub 2022 Jan 08. PMID: 35016937.
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Wear Resistance of the Glass-Fiber Reinforced Polymer Composite with the Addition of Quartz Filler.

Materials (Basel, Switzerland)

Zurowski W, Zepchło J, Krzyzak A, Gevorkyan E, Rucki M, Siek E, Białkowska A.
PMID: 34300741
Materials (Basel). 2021 Jul 08;14(14). doi: 10.3390/ma14143825.

The paper presents the results of investigations on the glass fiber reinforced composite for the floor panels with quartz powder additions of different percentages in terms of wear resistance, friction coefficient, hardness, and strength. The wear resistance was assessed...

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Zurowski W, Zepchło J, Krzyzak A, et al. Wear Resistance of the Glass-Fiber Reinforced Polymer Composite with the Addition of Quartz Filler. Materials (Basel). 2021;14(14)doi: 10.3390/ma14143825.
Zurowski, W., Zepchło, J., Krzyzak, A., Gevorkyan, E., Rucki, M., Siek, E., & Białkowska, A. (2021). Wear Resistance of the Glass-Fiber Reinforced Polymer Composite with the Addition of Quartz Filler. Materials (Basel, Switzerland), 14(14), . https://doi.org/10.3390/ma14143825
Zurowski, Wojciech, et al. "Wear Resistance of the Glass-Fiber Reinforced Polymer Composite with the Addition of Quartz Filler." Materials (Basel, Switzerland) vol. 14,14 (2021). doi: https://doi.org/10.3390/ma14143825
Zurowski W, Zepchło J, Krzyzak A, Gevorkyan E, Rucki M, Siek E, Białkowska A. Wear Resistance of the Glass-Fiber Reinforced Polymer Composite with the Addition of Quartz Filler. Materials (Basel). 2021 Jul 08;14(14). doi: 10.3390/ma14143825. PMID: 34300741; PMCID: PMC8304972.
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Analysis of the Electroconsolidation Process of Fine-Dispersed Structures Out of Hot Pressed Al.

Materials (Basel, Switzerland)

Gevorkyan E, Rucki M, Krzysiak Z, Chishkala V, Zurowski W, Kucharczyk W, Barsamyan V, Nerubatskyi V, Mazur T, Morozow D, Siemiątkowski Z, Caban J.
PMID: 34772030
Materials (Basel). 2021 Oct 29;14(21). doi: 10.3390/ma14216503.

Fabrication of alumina-tungsten carbide nanocomposite was investigated. Characteristics of the densification and sintering were analyzed considering both the nano-size particle starting powders and the processing stages. Different heating rates were generated during densification and consolidation with a maximal load...

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Gevorkyan E, Rucki M, Krzysiak Z, et al. Analysis of the Electroconsolidation Process of Fine-Dispersed Structures Out of Hot Pressed Al. Materials (Basel). 2021;14(21)doi: 10.3390/ma14216503.
Gevorkyan, E., Rucki, M., Krzysiak, Z., Chishkala, V., Zurowski, W., Kucharczyk, W., Barsamyan, V., Nerubatskyi, V., Mazur, T., Morozow, D., Siemiątkowski, Z., & Caban, J. (2021). Analysis of the Electroconsolidation Process of Fine-Dispersed Structures Out of Hot Pressed Al. Materials (Basel, Switzerland), 14(21), . https://doi.org/10.3390/ma14216503
Gevorkyan, Edwin, et al. "Analysis of the Electroconsolidation Process of Fine-Dispersed Structures Out of Hot Pressed Al." Materials (Basel, Switzerland) vol. 14,21 (2021). doi: https://doi.org/10.3390/ma14216503
Gevorkyan E, Rucki M, Krzysiak Z, Chishkala V, Zurowski W, Kucharczyk W, Barsamyan V, Nerubatskyi V, Mazur T, Morozow D, Siemiątkowski Z, Caban J. Analysis of the Electroconsolidation Process of Fine-Dispersed Structures Out of Hot Pressed Al. Materials (Basel). 2021 Oct 29;14(21). doi: 10.3390/ma14216503. PMID: 34772030; PMCID: PMC8585290.
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Do We Expect More from Radiology AI than from Radiologists?.

Radiology. Artificial intelligence

Mazurowski MA.
PMID: 34350411
Radiol Artif Intell. 2021 Mar 17;3(4):e200221. doi: 10.1148/ryai.2021200221. eCollection 2021 Jul.

The expectations of radiology artificial intelligence do not match expectations of radiologists in terms of performance and explainability.

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Mazurowski MA. Do We Expect More from Radiology AI than from Radiologists?. Radiol Artif Intell. 2021;3(4):e200221doi: 10.1148/ryai.2021200221.
Mazurowski, M. A. (2021). Do We Expect More from Radiology AI than from Radiologists?. Radiology. Artificial intelligence, 3(4), e200221. https://doi.org/10.1148/ryai.2021200221
Mazurowski, Maciej A. "Do We Expect More from Radiology AI than from Radiologists?." Radiology. Artificial intelligence vol. 3,4 (2021): e200221. doi: https://doi.org/10.1148/ryai.2021200221
Mazurowski MA. Do We Expect More from Radiology AI than from Radiologists?. Radiol Artif Intell. 2021 Mar 17;3(4):e200221. doi: 10.1148/ryai.2021200221. eCollection 2021 Jul. PMID: 34350411; PMCID: PMC8328102.
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Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images.

Radiology. Artificial intelligence

Buda M, AlBadawy EA, Saha A, Mazurowski MA.
PMID: 33937809
Radiol Artif Intell. 2020 Jan 29;2(1):e180050. doi: 10.1148/ryai.2019180050. eCollection 2020 Jan.

PURPOSE: To employ deep learning to predict genomic subtypes of lower-grade glioma (LLG) tumors based on their appearance at MRI.MATERIALS AND METHODS: Imaging data from The Cancer Imaging Archive and genomic data from The Cancer Genome Atlas from 110...

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Buda M, AlBadawy EA, Saha A, et al. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiol Artif Intell. 2020;2(1):e180050doi: 10.1148/ryai.2019180050.
Buda, M., AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2020). Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiology. Artificial intelligence, 2(1), e180050. https://doi.org/10.1148/ryai.2019180050
Buda, Mateusz, et al. "Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images." Radiology. Artificial intelligence vol. 2,1 (2020): e180050. doi: https://doi.org/10.1148/ryai.2019180050
Buda M, AlBadawy EA, Saha A, Mazurowski MA. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images. Radiol Artif Intell. 2020 Jan 29;2(1):e180050. doi: 10.1148/ryai.2019180050. eCollection 2020 Jan. PMID: 33937809; PMCID: PMC8017403.
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Performance appraisal reviews: face to face with employees.

Radiology management

Mazurowski J.
PMID: 10346646
Radiol Manage. 1999 Jan-Feb;21(1):12-4.

No abstract available.

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Mazurowski J. Performance appraisal reviews: face to face with employees. Radiol Manage. 1999;21(1):12-4
Mazurowski, J. (1999). Performance appraisal reviews: face to face with employees. Radiology management, 21(1), 12-4.
Mazurowski, J. "Performance appraisal reviews: face to face with employees." Radiology management vol. 21,1 (1999): 12-4.
Mazurowski J. Performance appraisal reviews: face to face with employees. Radiol Manage. 1999 Jan-Feb;21(1):12-4. PMID: 10346646.
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3D Pyramid Pooling Network for Abdominal MRI Series Classification.

IEEE transactions on pattern analysis and machine intelligence

Zhu Z, Mittendorf A, Shropshire E, Allen B, Miller C, Bashir MR, Mazurowski MA.
PMID: 33112740
IEEE Trans Pattern Anal Mach Intell. 2020 Oct 28; doi: 10.1109/TPAMI.2020.3033990. Epub 2020 Oct 28.

Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of Picture Archiving and Communication Systems (PACSs) and post-processing workstations. In this paper,...

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Zhu Z, Mittendorf A, Shropshire E, et al. 3D Pyramid Pooling Network for Abdominal MRI Series Classification. IEEE Trans Pattern Anal Mach Intell. 2020;doi: 10.1109/TPAMI.2020.3033990.
Zhu, Z., Mittendorf, A., Shropshire, E., Allen, B., Miller, C., Bashir, M. R., & Mazurowski, M. A. (2020). 3D Pyramid Pooling Network for Abdominal MRI Series Classification. IEEE transactions on pattern analysis and machine intelligence, . https://doi.org/10.1109/TPAMI.2020.3033990
Zhu, Zhe, et al. "3D Pyramid Pooling Network for Abdominal MRI Series Classification." IEEE transactions on pattern analysis and machine intelligence vol. (2020). doi: https://doi.org/10.1109/TPAMI.2020.3033990
Zhu Z, Mittendorf A, Shropshire E, Allen B, Miller C, Bashir MR, Mazurowski MA. 3D Pyramid Pooling Network for Abdominal MRI Series Classification. IEEE Trans Pattern Anal Mach Intell. 2020 Oct 28; doi: 10.1109/TPAMI.2020.3033990. Epub 2020 Oct 28. PMID: 33112740.
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Showing 1 to 12 of 22 entries