Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …
neural network, residual neural network, adversarial network; U-Net architectures are most …
Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray
Background and objective Automatic segmentation and annotation of medical image plays a
critical role in scientific research and the medical care community. Automatic segmentation …
critical role in scientific research and the medical care community. Automatic segmentation …
A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images
Large variations in anatomical shape and size, too much overlap between anatomical
structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x …
structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x …
An incremental learning approach to automatically recognize pulmonary diseases from the multi-vendor chest radiographs
The human respiratory network is a vital system that provides oxygen supply and
nourishment to the whole body. Pulmonary diseases can cause severe respiratory …
nourishment to the whole body. Pulmonary diseases can cause severe respiratory …
GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Segmentation of biomedical images is the method of semiautomatic and automatic detection
of boundaries within 2D and 3D images. The major challenge of medical image …
of boundaries within 2D and 3D images. The major challenge of medical image …
Incremental cross-domain adaptation for robust retinopathy screening via Bayesian deep learning
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause
severe visual impairments or even blindness. Many researchers have developed …
severe visual impairments or even blindness. Many researchers have developed …
A convolutional neural network architecture for segmentation of lung diseases using chest X-ray images
The segmentation of lungs from medical images is a critical step in the diagnosis and
treatment of lung diseases. Deep learning techniques have shown great promise in …
treatment of lung diseases. Deep learning techniques have shown great promise in …
Attention-guided convolutional neural network for detecting pneumonia on chest x-rays
B Li, G Kang, K Cheng, N Zhang - 2019 41st annual …, 2019 - ieeexplore.ieee.org
Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest
X-ray (CXR) examination. However, the high visual similarity between a large number of …
X-ray (CXR) examination. However, the high visual similarity between a large number of …
Improving lung region segmentation accuracy in chest X-ray images using a two-model deep learning ensemble approach
We propose a deep learning framework to improve segmentation accuracy of the lung
region in Chest X-Ray (CXR) images. The proposed methodology implements a “divide and …
region in Chest X-Ray (CXR) images. The proposed methodology implements a “divide and …
Automatic segmentation of pneumothorax in chest radiographs based on a two-stage deep learning method
Pneumothorax is common but a life-threatening thoracic disease, which is difficult to
diagnose based on chest X-ray images due to its subtle characteristics and low contrast of …
diagnose based on chest X-ray images due to its subtle characteristics and low contrast of …