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 …
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 …
M Abdel-Basset, V Chang, H Hawash… - Knowledge-Based …, 2021 - Elsevier
The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …
Z Luo, Z Jia, Z Yuan, J Peng - IEEE Journal of Biomedical and …, 2020 - ieeexplore.ieee.org
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 …
Y Ding, W Zheng, J Geng, Z Qin… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
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 …
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous …
X Zhong, C Ding, X Qu, D Tao - International Journal of Computer Vision, 2021 - Springer
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 …
Automatic and accurate pavement crack detection is essential for cost-effective road maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …