dc.contributor.author |
Ahmad, Parvez |
|
dc.contributor.author |
Qamar, Saqib |
|
dc.contributor.author |
Hashemi, Seyed Raein |
|
dc.contributor.author |
Shen, Linlin |
|
dc.contributor.editor |
Crimi, A |
|
dc.contributor.editor |
Bakas, S |
|
dc.coverage.spatial |
PEOPLES R CHINA, Shenzhen |
|
dc.date.accessioned |
2024-06-05T00:04:10Z |
|
dc.date.available |
2024-06-05T00:04:10Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
(2020). Lecture Notes in Computer Science, 11993, 158-166. |
|
dc.identifier.isbn |
9783030466428 |
|
dc.identifier.issn |
1611-3349 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/68552 |
|
dc.description.abstract |
The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent results against classical machine learning algorithms. In this paper, we present a unique approach to incorporate contexual information from multiple brain MRI labels. To address the problems of brain tumor segmentation, we implement combined strategies of residual-dense connections, multiple rates of an atrous convolutional layer on popular 3D U-Net architecture. To train and validate our proposed algorithm, we used BRATS 2019 different datasets. The results are promising on the different evaluation metrics. |
|
dc.publisher |
Springer Nature |
|
dc.relation.ispartof |
5th International MICCAI Brain-Lesion Workshop (BrainLes) |
|
dc.relation.ispartofseries |
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2019), PT II |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
46 Information and Computing Sciences |
|
dc.subject |
4611 Machine Learning |
|
dc.subject |
Cancer |
|
dc.subject |
Biomedical Imaging |
|
dc.subject |
Brain Disorders |
|
dc.subject |
Neurosciences |
|
dc.subject |
Neurological |
|
dc.subject |
Science & Technology |
|
dc.subject |
Technology |
|
dc.subject |
Life Sciences & Biomedicine |
|
dc.subject |
Computer Science, Artificial Intelligence |
|
dc.subject |
Computer Science, Interdisciplinary Applications |
|
dc.subject |
Radiology, Nuclear Medicine & Medical Imaging |
|
dc.subject |
Computer Science |
|
dc.subject |
Deep learning |
|
dc.subject |
Convolutional neural networks |
|
dc.subject |
Residual-dense connections |
|
dc.subject |
Atrous rates |
|
dc.subject |
Brain tumor segmentation |
|
dc.title |
Hybrid Labels for Brain Tumor Segmentation |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1007/978-3-030-46643-5_15 |
|
pubs.begin-page |
158 |
|
pubs.volume |
11993 |
|
dc.date.updated |
2024-05-31T06:42:02Z |
|
dc.rights.holder |
Copyright: Springer Nature Switzerland AG |
en |
pubs.end-page |
166 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2019-10-17 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RetrictedAccess |
en |
pubs.elements-id |
1029149 |
|
pubs.org-id |
Bioengineering Institute |
|
dc.identifier.eissn |
1611-3349 |
|
pubs.record-created-at-source-date |
2024-05-31 |
|
pubs.online-publication-date |
2020-05-19 |
|