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 …
Diffusion adversarial representation learning for self-supervised vessel segmentation
Vessel segmentation in medical images is one of the important tasks in the diagnosis of
vascular diseases and therapy planning. Although learning-based segmentation …
vascular diseases and therapy planning. Although learning-based segmentation …
Self-supervised semantic segmentation: Consistency over transformation
S Karimijafarbigloo, R Azad… - Proceedings of the …, 2023 - openaccess.thecvf.com
Accurate medical image segmentation is of utmost importance for enabling automated
clinical decision procedures. However, prevailing supervised deep learning approaches for …
clinical decision procedures. However, prevailing supervised deep learning approaches for …
Unsupervised skin lesion segmentation via structural entropy minimization on multi-scale superpixel graphs
Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The
complex features of pixels in the lesion region impede the lesion segmentation accuracy …
complex features of pixels in the lesion region impede the lesion segmentation accuracy …
Self-supervised representation learning framework for remote physiological measurement using spatiotemporal augmentation loss
Recent advances in supervised deep learning methods are enabling remote measurements
of photoplethysmographybased physiological signals using facial videos. The performance …
of photoplethysmographybased physiological signals using facial videos. The performance …
Self-supervised few-shot learning for semantic segmentation: An annotation-free approach
Few-shot semantic segmentation (FSS) offers immense potential in the field of medical
image analysis, enabling accurate object segmentation with limited training data. However …
image analysis, enabling accurate object segmentation with limited training data. However …
Camouflaged object segmentation based on joint salient object for contrastive learning
X Jiang, W Cai, Y Ding, X Wang, D Hong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In a broad sense, camouflaged objects generally refer to objects that have a high degree of
similarity to the background. Therefore, camouflaged object segmentation (COS) is more …
similarity to the background. Therefore, camouflaged object segmentation (COS) is more …
Ms-former: Multi-scale self-guided transformer for medical image segmentation
S Karimijafarbigloo, R Azad… - Medical Imaging with …, 2024 - proceedings.mlr.press
Multi-scale representations have proven to be a powerful tool since they can take into
account both the fine-grained details of objects in an image as well as the broader context …
account both the fine-grained details of objects in an image as well as the broader context …
USL-Net: Uncertainty self-learning network for unsupervised skin lesion segmentation
X Li, B Peng, J Hu, C Ma, D Yang, Z **e - Biomedical Signal Processing …, 2024 - Elsevier
Unsupervised skin lesion segmentation offers several benefits, such as conserving expert
human resources, reducing discrepancies caused by subjective human labeling, and …
human resources, reducing discrepancies caused by subjective human labeling, and …
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Deep neural networks have shown exemplary performance on semantic scene
understanding tasks on source domains but due to the absence of style diversity during …
understanding tasks on source domains but due to the absence of style diversity during …