Luke Macyszyn
Orcid: 0000-0002-3706-2796
According to our database1,
Luke Macyszyn
authored at least 14 papers
between 2016 and 2023.
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Bibliography
2023
Segmentation and identification of fatty infiltration of the erector spinae using deep learning.
Proceedings of the Medical Imaging 2023: Image Processing, 2023
2022
A deep network ensemble for segmentation of cervical spinal cord and neural foramina.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022
Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022
2021
Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.
Medical Image Anal., 2021
Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
2019
EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation.
CoRR, 2019
2018
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?
CoRR, 2018
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
A deep learning approach to spine segmentation using a feed-forward chain of pixel-wise convolutional networks.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
Cell Counting and Segmentation of Immunohistochemical Images in the Spinal Cord: Comparing Deep Learning and Traditional Approaches.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018
2017
Proceedings of the 13th International Symposium on Medical Information Processing and Analysis, 2017
Automatic vertebral bodies detection of x-ray images using invariant multiscale template matching.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
2016
Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, 2016