Generative adversarial networks in medical image segmentation: A review
S Xun, D Li, H Zhu, M Chen, J Wang, J Li… - Computers in biology …, 2022 - Elsevier
Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …
of deep learning in 2014, it has received extensive attention from academia and industry …
Doubleu-net: A deep convolutional neural network for medical image segmentation
Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …
A systematic review of deep learning based image segmentation to detect polyp
Among the world's most common cancers, colorectal cancer is the third most severe form of
cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective …
cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective …
A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation
Colonoscopy is considered the gold standard for detection of colorectal cancer and its
precursors. Existing examination methods are, however, hampered by high overall miss …
precursors. Existing examination methods are, however, hampered by high overall miss …
[HTML][HTML] Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy
Background Colonoscopy remains the gold-standard screening for colorectal cancer.
However, significant miss rates for polyps have been reported, particularly when there are …
However, significant miss rates for polyps have been reported, particularly when there are …
Attention based multi-scale parallel network for polyp segmentation
P Song, J Li, H Fan - Computers in Biology and Medicine, 2022 - Elsevier
Colonoscopy is an effective method for detecting colorectal polyps and preventing colorectal
cancer. Therefore, in clinical practice, it is very important to accurately segment the location …
cancer. Therefore, in clinical practice, it is very important to accurately segment the location …
Tmd-unet: Triple-unet with multi-scale input features and dense skip connection for medical image segmentation
Deep learning is one of the most effective approaches to medical image processing
applications. Network models are being studied more and more for medical image …
applications. Network models are being studied more and more for medical image …
Learn to threshold: Thresholdnet with confidence-guided manifold mixup for polyp segmentation
The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and
cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation …
cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation …
[HTML][HTML] Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …
cancer based on imaging data continue to pose significant challenges. These include inter …
Semantic segmentation of digestive abnormalities from wce images by using attresu-net architecture
Colorectal cancer is one of the most common malignancies and the leading cause of cancer
death worldwide. Wireless capsule endoscopy is currently the most frequent method for …
death worldwide. Wireless capsule endoscopy is currently the most frequent method for …