Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningPositionSemantic SegmentationSimilar Papers 제목 키워드 기반
Segmentation of 2D Brain MR Images
Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manu…
Image SegmentationSegmentationSemantic SegmentationIterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI
In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients. An initial Bayesian tumour…
SegmentationTumour ClassificationBrain tumour segmentation using a triplanar ensemble of U-Nets
Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater vari…
Brain Tumor SegmentationSegmentationTumor SegmentationAutomated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks
Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automat…
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing a…
Generative Adversarial NetworkPrognosisSegmentationSemantic Segmentation+1