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Disambiguation of "Brosch, Tom"

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Spinal cord grey matter segmentation challenge.

, , , , , , , , , , , , , , , , , , , , , , , , , , , and . NeuroImage, (2017)

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Joseph Brosch

Sven Brosch

Dieter Brosch

Hans Brosch

Erich Brosch

 

Other publications of authors with the same name

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks., , , , , , , , and . BMC Medical Imaging, 21 (1): 69 (2021)Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections., , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 330-337. Springer, (2017)Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation., , , , , and . IEEE Trans. Medical Imaging, 35 (5): 1229-1239 (2016)Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Manifold Learning of Brain MRIs by Deep Learning., and . MICCAI (2), volume 8150 of Lecture Notes in Computer Science, page 633-640. Springer, (2013)Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation., , , , and . MLMI, volume 8679 of Lecture Notes in Computer Science, page 117-124. Springer, (2014)Spine centerline extraction and efficient spine reading of MRI and CT data., , , and . Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 1057425. SPIE, (2018)Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease., , , , , , , and . Medical Image Anal., (2022)Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images., and . Neural Comput., 27 (1): 211-227 (2015)Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis., , , , , , and . LABELS/DLMIA@MICCAI, volume 10008 of Lecture Notes in Computer Science, page 86-94. (2016)