37 citations · 53 across the 7 of their papers we have counts for
7 papers
Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Changlu Guo, Jiangyan Dai, Marton Szemenyei +1
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. Lesion segmentation is cru…
3D SA-UNet: 3D Spatial Attention UNet with 3D Atrous Spatial Pyramid Pooling for White Matter Hyperintensities Segmentation
Changlu Guo
White Matter Hyperintensity (WMH) is an imaging feature related to various diseases such as dementia and stroke. Accurately segmenting WMH using computer technology is crucial for…
Minimizing false negative rate in melanoma detection and providing insight into the causes of classification
Ellák Somfai, Benjámin Baffy, Kristian Fenech +8
Our goal is to bridge human and machine intelligence in melanoma detection. We develop a classification system exploiting a combination of visual pre-processing, deep learning, and…
Residual Spatial Attention Network for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yugen Yi +2
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascu…
Dense Residual Network for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yugen Yi +3
Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such…
Channel Attention Residual U-Net for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yangtao Hu +3
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Resid…