Dual-term loss function for shape-aware medical image segmentation
Q Huang, Y Zhou, L Tao - 2021 IEEE 18th International …, 2021 - ieeexplore.ieee.org
Besides network architecture, researchers have recently focused their attention on the loss
function for the Convolutional Neural Network-based medical image segmentation. The loss …
function for the Convolutional Neural Network-based medical image segmentation. The loss …
A deep model towards accurate boundary location and strong generalization for medical image segmentation
B Wang, P Geng, T Li, Y Yang, X Tian, G Zhang… - … Signal Processing and …, 2024 - Elsevier
Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis
and monitoring. However, due to the complexity of medical images and the limitations of …
and monitoring. However, due to the complexity of medical images and the limitations of …
Dual consistency loss for contour-aware segmentation in medical images
Medical image segmentation is a paramount task for several clinical applications, namely for
the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries …
the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries …
Fully convolutional attention network for biomedical image segmentation
In this paper, we embed two types of attention modules in the dilated fully convolutional
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …
ConvUNeXt: An efficient convolution neural network for medical image segmentation
Recently, ConvNeXts constructing from standard ConvNet modules has produced
competitive performance in various image applications. In this paper, an efficient model …
competitive performance in various image applications. In this paper, an efficient model …
DRU-Net: an efficient deep convolutional neural network for medical image segmentation
Residual network (ResNet) and densely connected network (DenseNet) have significantly
improved the training efficiency and performance of deep convolutional neural networks …
improved the training efficiency and performance of deep convolutional neural networks …
MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …
strong need for automatic medical image segmentation. Still, current image segmentation …
Conv-MCD: A plug-and-play multi-task module for medical image segmentation
B Murugesan, K Sarveswaran… - Machine Learning in …, 2019 - Springer
For the task of medical image segmentation, fully convolutional network (FCN) based
architectures have been extensively used with various modifications. A rising trend in these …
architectures have been extensively used with various modifications. A rising trend in these …
High-level prior-based loss functions for medical image segmentation: A survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …
performance for supervised medical image segmentation, across various imaging modalities …
MS UX-Net: A Multi-scale Depth-Wise Convolution Network for Medical Image Segmentation
M Zhang, Z Xu, Q Yang, D Zhang - Chinese Conference on Pattern …, 2023 - Springer
Semantic segmentation of 3D medical images plays an important role in assisting
physicians in diagnosing and successively studying the progression of the disease. In recent …
physicians in diagnosing and successively studying the progression of the disease. In recent …