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| id | 20fb032f-7a5b-4a2b-9ab8-367ee53030ea |
| application_area | Medical Imaging, Segmentation |
| task | Brain Tumor Segmentation |
| task_extended | Brain tumor segmentation for the BraTS 18 challenge |
| data_type | Nifti-1 volumes |
| data_source | www.braintumorsegmentation.org |
| title | Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation |
| source | International MICCAI Brainlesion Workshop |
| url | https://link.springer.com/chapter/10.1007/978-3-030-11726-9_40 |
| year | 2018 |
| authors | Richard McKinley, Raphael Meier, Roland Wiest |
| abstract | We introduce a new family of classifiers based on our previous DeepSCAN architecture, in which densely connected blocks of dilated convolutions are embedded in a shallow U-net-style structure of down/upsampling and skip connections. These networks are trained using a newly designed loss function which models label noise and uncertainty. We present results on the testing dataset of the Multimodal Brain Tumor Segmentation Challenge 2018. |
| google_scholar | https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2018&q=Ensembles+of+densely-connected+CNNs+with+label-uncertainty+for+brain+tumor+segmentation&btnG= |
| bibtex | @inproceedings{mckinley2018ensembles,title={Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation},author={McKinley, Richard and Meier, Raphael and Wiest, Roland},booktitle={International MICCAI Brainlesion Workshop},pages={456--465},year={2018},organization={Springer}} |
| description | Densely-Connected CNNs |
| provenance | |
| architecture | CNN |
| learning_type | Supervised |
| format | .pth.tar |
| I/O | model I/O can be viewed here |
| license | model license can be viewed here |
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