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Found 7 papers, 5 papers with code
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension.
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Retinal Vessel Segmentation
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Residual Spatial Attention Network for Retinal Vessel Segmentation
In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation.
3D SA-UNet: 3D Spatial Attention UNet with 3D ASPP for White Matter Hyperintensities Segmentation
Additionally, to capture features at different scales, we extend the Atrous Spatial Pyramid Pooling (ASPP) module to a 3D version, enhancing the segmentation performance of the network.
Channel Attention Residual U-Net for Retinal Vessel Segmentation
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases.
Dense Residual Network for Retinal Vessel Segmentation
But for other retinal imaging modalities, more research is needed to explore vascular extraction.
Minimizing false negative rate in melanoma detection and providing insight into the causes of classification
Our goal is to bridge human and machine intelligence in melanoma detection.
Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Skin cancer is a frequently occurring cancer in the human population, and it is very important to be able to diagnose malignant tumors in the body early.
