Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation
L Fang, X Wang - Biomedical Signal Processing and Control, 2023 - Elsevier
With the growth of data information and the development of computer equipment, it is
extremely time-consuming and laborious to rely on the traditional manual segmentation of …
extremely time-consuming and laborious to rely on the traditional manual segmentation of …
Attention mechanisms in medical image segmentation: A survey
Medical image segmentation plays an important role in computer-aided diagnosis. Attention
mechanisms that distinguish important parts from irrelevant parts have been widely used in …
mechanisms that distinguish important parts from irrelevant parts have been widely used in …
Sketch assisted face image coding for human and machine vision: a joint training approach
Image coding is one of the most fundamental techniques and is widely used in image/video
processing and multimedia communications. Current image coding methods are mainly …
processing and multimedia communications. Current image coding methods are mainly …
A dual tri-path CNN system for brain tumor segmentation
J Tong, C Wang - Biomedical Signal Processing and Control, 2023 - Elsevier
The research on developing CNN-based fully-automated brain-tumor-segmentation systems
has been progressing rapidly. For the systems to be applicable in practice, a good …
has been progressing rapidly. For the systems to be applicable in practice, a good …
SEResU-Net for multimodal brain tumor segmentation
C Yan, J Ding, H Zhang, K Tong, B Hua, S Shi - IEEE Access, 2022 - ieeexplore.ieee.org
Glioma is the most common type of brain tumor, and it has a high mortality rate. Accurate
tumor segmentation based on magnetic resonance imaging (MRI) is of great significance for …
tumor segmentation based on magnetic resonance imaging (MRI) is of great significance for …
MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation
Accurate localization of surgical instruments is of utmost importance for precise robot-
assisted surgeries. With the development of artificial intelligence, deep convolutional neural …
assisted surgeries. With the development of artificial intelligence, deep convolutional neural …
Exploiting partial common information microstructure for multi-modal brain tumor segmentation
Y Mei, G Venkataramani, T Lan - Workshop on Machine Learning for …, 2023 - Springer
Learning with multiple modalities is crucial for automated brain tumor segmentation from
magnetic resonance imaging data. Explicitly optimizing the common information shared …
magnetic resonance imaging data. Explicitly optimizing the common information shared …
[HTML][HTML] High-resolution semantic segmentation of woodland fires using residual attention UNet and time series of Sentinel-2
Southern Africa experiences a great number of wildfires, but the dependence on low-
resolution products to detect and quantify fires means both that there is a time lag and that …
resolution products to detect and quantify fires means both that there is a time lag and that …
Hybrid-DANet: an encoder-decoder based hybrid weights alignment with multi-dilated attention network for automatic brain tumor segmentation
Gliomas are the most common and highly growing tumors lead to high mortality rate in their
highest grade. The early diagnosis of gliomas, and treatment planning are most important …
highest grade. The early diagnosis of gliomas, and treatment planning are most important …
Inception-UDet: an improved U-Net architecture for brain tumor segmentation
Brain tumor segmentation is an important field and a sensitive task in tumor diagnosis. The
treatment research in this area has helped specialists in detecting the tumor's location in …
treatment research in this area has helped specialists in detecting the tumor's location in …