Luke Macyszyn

发表

Abstract A26: Translation of isoform-level gene signature into robust diagnostic assay for glioblastoma subtyping.

Luke Macyszyn, Louise C. Showe, Ramana V. Davuluri, 2013 .

Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images

Bilwaj Gaonkar, Luke Macyszyn, Yihao Xia, 2017, IEEE Journal of Translational Engineering in Health and Medicine.

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

Bilwaj Gaonkar, Luke Macyszyn, Neil Martin, 2016, SPIE Medical Imaging.

A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks

Bilwaj Gaonkar, Luke Macyszyn, Steven Moran, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Automated tumor volumetry using computer-aided image segmentation.

Bilwaj Gaonkar, Luke Macyszyn, Christos Davatzikos, 2015, Academic radiology.

Deep learning for medical image segmentation – using the IBM TrueNorth neurosynaptic system

Bilwaj Gaonkar, Luke Macyszyn, William Whitehead, 2018, Medical Imaging.

Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan? pdf

Bilwaj Gaonkar, Luke Macyszyn, Matthew A. Brown, 2018, ArXiv.

Improving White Matter Tractography by Resolving the Challenges of Edema

Luke Macyszyn, Ragini Verma, Jérémy Lecoeur, 2013 .

Bayesian automated cortical segmentation for neonatal MRI

Bilwaj Gaonkar, Luke Macyszyn, Yalin Wang, 2017, Symposium on Medical Information Processing and Analysis.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation

Bilwaj Gaonkar, Luke Macyszyn, Mark Attiah, 2020, Medical Image Anal..

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation pdf

Bilwaj Gaonkar, Luke Macyszyn, Alex Bui, 2019, ArXiv.

Orchestral fully convolutional networks for small lesion segmentation in brain MRI

Bilwaj Gaonkar, Luke Macyszyn, Thomas D. Coates, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Addressing the Challenge of Edema in Fiber Tracking

Luke Macyszyn, Ragini Verma, Mark A. Elliott, 2014 .

Ethical Issues Arising Due to Bias in Training A.I. Algorithms in Healthcare and Data Sharing as a Potential Solution

Bilwaj Gaonkar, Luke Macyszyn, Kirstin Kim, 2020, AI Ethics Journal.

Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching

Mona Sharifi Sarabi, Christine S. Ahn, Diana Babayan, 2017, Medical Imaging.

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

Bilwaj Gaonkar, Luke Macyszyn, Steven Moran, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation

Bilwaj Gaonkar, Luke Macyszyn, Alex A. T. Bui, 2020, Medical Image Anal..

Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer

Luke Macyszyn, Ien Li, Kirstin Cook, 2021, Medical Imaging.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Luke Macyszyn, Christos Davatzikos, Yingtao Bi, 2016, Neuro-oncology.

Implementation of a departmental picture archiving and communication system: a productivity and cost analysis.

Luke Macyszyn, Sean M. Grady, Leif-Erik Bohman, 2013, Neurosurgery.

135 Imaging Patterns Predict Patient Survival and Molecular Subtype in Glioblastoma Using Machine Learning Techniques

Luke Macyszyn, Yingtao Bi, Sharmistha Pal, 2015 .

Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity.

Luke Macyszyn, Christos Davatzikos, Ragini Verma, 2014, Radiology.

Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes

Luke Macyszyn, Louise C. Showe, Ramana V. Davuluri, 2014, Nucleic acids research.

Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning.

Luke Macyszyn, Ragini Verma, Drew Parker, 2016, Neurosurgery.