Medical image segmentation using deep learning: A survey
Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …
papers has been presented recording the success of deep learning in the field. A …
[HTML][HTML] Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging
Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …
and biomedical images is a crucial early step in automatic image interpretation associated to …
ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images
For the clinical diagnosis, it is essential to obtain accurate morphology data of retinal blood
vessels from patients, and the morphology of retinal blood vessels can well help doctors to …
vessels from patients, and the morphology of retinal blood vessels can well help doctors to …
Scs-net: A scale and context sensitive network for retinal vessel segmentation
Accurately segmenting retinal vessel from retinal images is essential for the detection and
diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large …
diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large …
Reducing the hausdorff distance in medical image segmentation with convolutional neural networks
D Karimi, SE Salcudean - IEEE Transactions on medical …, 2019 - ieeexplore.ieee.org
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation
methods. However, the existing segmentation methods do not attempt to reduce HD directly …
methods. However, the existing segmentation methods do not attempt to reduce HD directly …
Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation
Abstract Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the
most notable showcases where deep learning technologies display their impressive …
most notable showcases where deep learning technologies display their impressive …
Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation
Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably …
Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning
Background/aims Fundus fluorescein angiography (FFA) is an important technique to
evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA …
evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA …
A review on the use of deep learning for medical images segmentation
M Aljabri, M AlGhamdi - Neurocomputing, 2022 - Elsevier
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …