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Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.

B. Pham, B. Gaonkar, W. Whitehead, S. Moran, Q. Dai, L. Macyszyn, and V. Edgerton. EMBC, page 842-845. IEEE, (2018)

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Friedmar Lüke

Werner Luke

Alfred Lüke

Luke Ndubuisi

Carola Lüke

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Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation.B. Gaonkar, D. Hovda, N. Martin, and L. Macyszyn. Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97852I. SPIE, (2016)Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets.A. Ratnaparkhi, B. Gaonkar, D. Zarrin, I. Li, K. Cook, A. Laiwalla, B. Wilson, M. Attiah, C. Ahn, D. Villaroman and 4 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer.I. Li, K. Cook, M. Le, B. Gaonkar, and L. Macyszyn. Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks.W. Whitehead, S. Moran, B. Gaonkar, L. Macyszyn, and S. Iyer. ISBI, page 868-871. IEEE, (2018)Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.B. Gaonkar, J. Beckett, M. Attiah, C. Ahn, M. Edwards, B. Wilson, A. Laiwalla, B. Salehi, B. Yoo, A. Bui and 1 other author(s). Medical Image Anal., (2021)Segmentation and identification of fatty infiltration of the erector spinae using deep learning.A. Ratnaparkhi, B. Gaonkar, A. Laiwalla, E. Berdy, M. Jafari, A. Suri, D. Zarrin, K. Cook, B. Wilson, L. Macyszyn and 1 other author(s). Medical Imaging: Image Processing, volume 12464 of SPIE Proceedings, SPIE, (2023)Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching.M. Sarabi, D. Villaroman, J. Beckett, M. Attiah, L. Marcus, C. Ahn, D. Babayan, B. Gaonkar, L. Macyszyn, and C. Raghavendra. Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 1013447. SPIE, (2017)A deep network ensemble for segmentation of cervical spinal cord and neural foramina.D. Zarrin, A. Ratnaparkhi, B. Wilson, K. Cook, I. Li, A. Laiwalla, M. Attiah, J. Beckett, B. Gaonkar, and L. Macyszyn. Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Orchestral fully convolutional networks for small lesion segmentation in brain MRI.B. Xu, Y. Chai, C. Galarza, C. Vu, B. Tamrazi, B. Gaonkar, L. Macyszyn, T. Jr., N. Leporé, and J. Wood. ISBI, page 889-892. IEEE, (2018)Automatic segmentation of lumbar vertebrae in CT images.A. Kulkarni, A. Raina, M. Sarabi, C. Ahn, D. Babayan, B. Gaonkar, L. Macyszyn, and C. Raghavendra. Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 1013448. SPIE, (2017)

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Disambiguation of "Macyszyn, Luke"

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Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.

Please choose a person to relate this publication to

Friedmar Lüke

Werner Luke

Alfred Lüke

Luke Ndubuisi

Carola Lüke

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Macyszyn, Luke"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.

Please choose a person to relate this publication to

Friedmar Lüke

Werner Luke

Alfred Lüke

Luke Ndubuisi

Carola Lüke

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
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Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.