Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012–2018 challenges
M Ghaffari, A Sowmya, R Oliver - IEEE reviews in biomedical …, 2019 - ieeexplore.ieee.org
Reliable brain tumor segmentation is essential for accurate diagnosis and treatment
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
A deep learning approach for intrusion detection in Internet of Things using focal loss function
Abstract Internet of Things (IoT) is likely to revolutionize healthcare, energy, education,
transportation, manufacturing, military, agriculture, and other industries. However, for the …
transportation, manufacturing, military, agriculture, and other industries. However, for the …
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
X Liu, L Qu, Z Xie, J Zhao, Y Shi, Z Song - arXiv preprint arXiv:2303.00232, 2023 - arxiv.org
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …
Dual focal loss to address class imbalance in semantic segmentation
MS Hossain, JM Betts, AP Paplinski - Neurocomputing, 2021 - Elsevier
A common problem in pixelwise classification or semantic segmentation is class imbalance,
which tends to reduce the classification accuracy of minority-class regions. An effective way …
which tends to reduce the classification accuracy of minority-class regions. An effective way …
A deep learning framework for spatiotemporal ultrasound localization microscopy
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few
micrometers. To achieve such performance, microbubble contrast agents must perfuse the …
micrometers. To achieve such performance, microbubble contrast agents must perfuse the …
An improved dice loss for pneumothorax segmentation by mining the information of negative areas
L Wang, C Wang, Z Sun, S Chen - IEEE Access, 2020 - ieeexplore.ieee.org
The lesion regions of a medical image account for only a small part of the image, and a
critical imbalance exists in the distribution of the positive and negative samples, which …
critical imbalance exists in the distribution of the positive and negative samples, which …
SoftSeg: Advantages of soft versus binary training for image segmentation
Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …
classification task per pixel. However, in applications such as medical imaging, this “black …
Kidney and renal tumor segmentation using a hybrid V-Net-Based model
Kidney tumors represent a type of cancer that people of advanced age are more likely to
develop. For this reason, it is important to exercise caution and provide diagnostic tests in …
develop. For this reason, it is important to exercise caution and provide diagnostic tests in …
3D parallel fully convolutional networks for real-time video wildfire smoke detection
X Li, Z Chen, QMJ Wu, C Liu - IEEE Transactions on Circuits …, 2018 - ieeexplore.ieee.org
Wildfires have devastating consequences on ecological systems and human lives. Accurate
and fast wildfire detection is crucial to reduce damage. The existing smoke detection …
and fast wildfire detection is crucial to reduce damage. The existing smoke detection …
Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images
Automated brain tumour segmentation from post-operative images is a clinically relevant yet
challenging problem. In this study, an automated method for segmenting brain tumour into …
challenging problem. In this study, an automated method for segmenting brain tumour into …