Deep learning for brain tumor segmentation: a survey of state-of-the-art
Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
Cross-modality deep feature learning for brain tumor segmentation
Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
Role of deep learning in classification of brain MRI images for prediction of disorders: a survey of emerging trends
PR Verma, AK Bhandari - Archives of Computational Methods in …, 2023 - Springer
Image classification is the act of labeling groups of pixels or voxels of an image based on
some rules. It finds applications in medical image analysis, and satellite image identification …
some rules. It finds applications in medical image analysis, and satellite image identification …
FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection
The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
HDC-Net: Hierarchical decoupled convolution network for brain tumor segmentation
Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
Deep learning–based detection and segmentation-assisted management of brain metastases
Background Three-dimensional T1 magnetization prepared rapid acquisition gradient echo
(3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We …
(3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We …
MVFusFra: A multi-view dynamic fusion framework for multimodal brain tumor segmentation
Medical practitioners generally rely on multimodal brain images, for example based on the
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …
Canet: Context aware network for brain glioma segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision,
progression monitoring and surgery planning. Based on deep neural networks, previous …
progression monitoring and surgery planning. Based on deep neural networks, previous …
Polysemy deciphering network for robust human–object interaction detection
Abstract Human–Object Interaction (HOI) detection is important to human-centric scene
understanding tasks. Existing works tend to assume that the same verb has similar visual …
understanding tasks. Existing works tend to assume that the same verb has similar visual …
Weakly supervised pavement crack semantic segmentation based on multi-scale object localization and incremental annotation refinement
Automatic and accurate pavement crack detection is essential for cost-effective road
maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …
maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …