[PDF][PDF] A review of self-supervised learning methods in the field of medical image analysis
J Xu - International Journal of Image, Graphics and Signal …, 2021 - mecs-press.org
In the field of medical image analysis, supervised deep learning strategies have achieved
significant development, while these methods rely on large labeled datasets. Self …
significant development, while these methods rely on large labeled datasets. Self …
A systematic review of automated segmentation methods and public datasets for the lung and its lobes and findings on computed tomography images
Objectives: Automated computational segmentation of the lung and its lobes and findings in
X-Ray based computed tomography (CT) images is a challenging problem with important …
X-Ray based computed tomography (CT) images is a challenging problem with important …
Multi-task deep model with margin ranking loss for lung nodule analysis
Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung
nodule is of great importance for therapeutic treatment and saving lives. Automated lung …
nodule is of great importance for therapeutic treatment and saving lives. Automated lung …
Surrogate supervision for medical image analysis: Effective deep learning from limited quantities of labeled data
N Tajbakhsh, Y Hu, J Cao, X Yan, Y Xiao… - 2019 IEEE 16th …, 2019 - ieeexplore.ieee.org
We investigate the effectiveness of a simple solution to the common problem of deep
learning in medical image analysis with limited quantities of labeled training data. The …
learning in medical image analysis with limited quantities of labeled training data. The …
CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network
The purpose of this study was to develop a fully-automated segmentation algorithm, robust
to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of …
to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of …
A two-stage CNN method for MRI image segmentation of prostate with lesion
Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate
cancer and other prostate diseases. The automatic segmentation of images from prostate …
cancer and other prostate diseases. The automatic segmentation of images from prostate …
Deep active lesion segmentation
Lesion segmentation is an important problem in computer-assisted diagnosis that remains
challenging due to the prevalence of low contrast, irregular boundaries that are unamenable …
challenging due to the prevalence of low contrast, irregular boundaries that are unamenable …
Liver tumor segmentation using 2.5 D UV-Net with multi-scale convolution
C Zhang, Q Hua, Y Chu, P Wang - Computers in Biology and Medicine, 2021 - Elsevier
Liver tumor segmentation networks are generally based on U-shaped encoder-decoder
network with 2D or 3D structure. However, 2D networks lose the inter-layer information of …
network with 2D or 3D structure. However, 2D networks lose the inter-layer information of …
A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images
Background and objective Lobectomy is a curative treatment for localized lung cancer. The
study aims to construct an automatic pipeline for segmenting pulmonary lobes before and …
study aims to construct an automatic pipeline for segmenting pulmonary lobes before and …
Semi-supervised multi-task learning with chest X-ray images
AAZ Imran, D Terzopoulos - Machine Learning in Medical Imaging: 10th …, 2019 - Springer
Discriminative models that require full supervision are inefficacious in the medical imaging
domain when large labeled datasets are unavailable. By contrast, generative modeling—ie …
domain when large labeled datasets are unavailable. By contrast, generative modeling—ie …