Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state‐of‐art applications
H Seo, M Badiei Khuzani, V Vasudevan… - Medical …, 2020 - Wiley Online Library
In recent years, significant progress has been made in develo** more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …
efficient machine learning algorithms for segmentation of medical and natural images. In this …
Ce-net: Context encoder network for 2d medical image segmentation
Medical image segmentation is an important step in medical image analysis. With the rapid
development of a convolutional neural network in image processing, deep learning has …
development of a convolutional neural network in image processing, deep learning has …
Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy
Diabetic retinopathy is ophthalmological distress, diabetic patients suffer due to clots,
lesions, or haemorrhage formation in the light-sensitive region of the retina. Blocking of …
lesions, or haemorrhage formation in the light-sensitive region of the retina. Blocking of …
C-cam: Causal cam for weakly supervised semantic segmentation on medical image
Recently, many excellent weakly supervised semantic segmentation (WSSS) works are
proposed based on class activation map** (CAM). However, there are few works that …
proposed based on class activation map** (CAM). However, there are few works that …
Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images
Automated prostate segmentation from 3D MR images is very challenging due to large
variations of prostate shape and indistinct prostate boundaries. We propose a novel …
variations of prostate shape and indistinct prostate boundaries. We propose a novel …
Boundary-weighted domain adaptive neural network for prostate MR image segmentation
Accurate segmentation of the prostate from magnetic resonance (MR) images provides
useful information for prostate cancer diagnosis and treatment. However, automated …
useful information for prostate cancer diagnosis and treatment. However, automated …
[PDF][PDF] Applications and datasets for superpixel techniques: A survey
The use of superpixels instead of pixels can significantly improve the speed of the current
pixel-based algorithms, and can even produce better results in many applications such as …
pixel-based algorithms, and can even produce better results in many applications such as …
Msu-net: Multi-scale u-net for 2d medical image segmentation
R Su, D Zhang, J Liu, C Cheng - Frontiers in Genetics, 2021 - frontiersin.org
Aiming at the limitation of the convolution kernel with a fixed receptive field and unknown
prior to optimal network width in U-Net, multi-scale U-Net (MSU-Net) is proposed by us for …
prior to optimal network width in U-Net, multi-scale U-Net (MSU-Net) is proposed by us for …
Edldr: An ensemble deep learning technique for detection and classification of diabetic retinopathy
Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood
vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region …
vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region …
3D APA-Net: 3D adversarial pyramid anisotropic convolutional network for prostate segmentation in MR images
Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR)
imaging has critical importance for the diagnosis and treatment of prostate diseases …
imaging has critical importance for the diagnosis and treatment of prostate diseases …