A survey on deep learning for skin lesion segmentation
Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …
FAT-Net: Feature adaptive transformers for automated skin lesion segmentation
Skin lesion segmentation from dermoscopic image is essential for improving the quantitative
analysis of melanoma. However, it is still a challenging task due to the large scale variations …
analysis of melanoma. However, it is still a challenging task due to the large scale variations …
EIU-Net: Enhanced feature extraction and improved skip connections in U-Net for skin lesion segmentation
Z Yu, L Yu, W Zheng, S Wang - Computers in Biology and Medicine, 2023 - Elsevier
Skin lesion segmentation is a computer-aided diagnosis method for quantitative analysis of
melanoma that can improve efficiency and accuracy. Although many methods based on U …
melanoma that can improve efficiency and accuracy. Although many methods based on U …
Self-loop uncertainty: A novel pseudo-label for semi-supervised medical image segmentation
Witnessing the success of deep learning neural networks in natural image processing, an
increasing number of studies have been proposed to develop deep-learning-based …
increasing number of studies have been proposed to develop deep-learning-based …
Malignant skin melanoma detection using image augmentation by oversamplingin nonlinear lower-dimensional embedding manifold
OO Abayomi-Alli, R Damasevicius… - Turkish Journal of …, 2021 - journals.tubitak.gov.tr
The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in
a high mortality rate of the affected patients due to late detection. Some challenges affecting …
a high mortality rate of the affected patients due to late detection. Some challenges affecting …
[HTML][HTML] Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN
Abstract Fully Convolution Networks have recently become popular for tackling semantic
segmentation problems. However, its performance is dependent on the hyper-parameters it …
segmentation problems. However, its performance is dependent on the hyper-parameters it …
[HTML][HTML] Analyzing malaria disease using effective deep learning approach
K Sriporn, CF Tsai, CE Tsai, P Wang - Diagnostics, 2020 - mdpi.com
Medical tools used to bolster decision-making by medical specialists who offer malaria
treatment include image processing equipment and a computer-aided diagnostic system …
treatment include image processing equipment and a computer-aided diagnostic system …
Challenges and recent solutions for image segmentation in the era of deep learning
E Goceri - 2019 ninth international conference on image …, 2019 - ieeexplore.ieee.org
Image segmentation has a key role in computer vision and image processing. Superiority of
deep learning based segmentation techniques has been shown in various studies in the …
deep learning based segmentation techniques has been shown in various studies in the …
Knowledge-aware deep framework for collaborative skin lesion segmentation and melanoma recognition
Deep learning techniques have shown their superior performance in dermatologist clinical
inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty …
inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty …
[HTML][HTML] A review of predictive and contrastive self-supervised learning for medical images
WC Wang, E Ahn, D Feng, J Kim - Machine Intelligence Research, 2023 - Springer
Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …
progressing significantly on computer vision tasks. But, the application of deep learning in …