EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT
J Wang, X Zhang, P Lv, L Zhou, H Wang - arXiv preprint arXiv:2110.01014, 2021 - arxiv.org
Purpose: This paper proposes a new network framework called EAR-U-Net, which
leverages EfficientNetB4, attention gate, and residual learning techniques to achieve …
leverages EfficientNetB4, attention gate, and residual learning techniques to achieve …
Towards simultaneous segmentation of liver tumors and intrahepatic vessels via cross-attention mechanism
Accurate visualization of liver tumors and their surrounding blood vessels is essential for
noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation …
noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation …
2D-densely connected convolution neural networks for automatic liver and tumor segmentation
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of
liver and its tumors in CT (Computed Tomography) images using densely connected fully …
liver and its tumors in CT (Computed Tomography) images using densely connected fully …
Mtanet: Multi-task attention network for automatic medical image segmentation and classification
Y Ling, Y Wang, W Dai, J Yu, P Liang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Medical image segmentation and classification are two of the most key steps in computer-
aided clinical diagnosis. The region of interest were usually segmented in a proper manner …
aided clinical diagnosis. The region of interest were usually segmented in a proper manner …
CR-U-Net: Cascaded U-net with residual mapping for liver segmentation in CT images
Y Liu, N Qi, Q Zhu, W Li - 2019 IEEE Visual Communications …, 2019 - ieeexplore.ieee.org
Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver
segmentation in CT scan is important for diagnosis and analysis of liver lesions. However …
segmentation in CT scan is important for diagnosis and analysis of liver lesions. However …
Variance‐aware attention U‐Net for multi‐organ segmentation
Purpose With the continuous development of deep learning based medical image
segmentation technology, it is expected to attain more robust and accurate performance for …
segmentation technology, it is expected to attain more robust and accurate performance for …
Hybrid cascaded neural network for liver lesion segmentation
Automatic liver lesion segmentation is a challenging task while having a significant impact
on assisting medical professionals in the designing of effective treatment and planning …
on assisting medical professionals in the designing of effective treatment and planning …
A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT
ST Tran, CH Cheng, DG Liu - IEEE Access, 2020 - ieeexplore.ieee.org
Medical image segmentation is one of the crucial tasks in diagnosis as well as pre-surgery.
Recently, deep learning has significantly contributed to improving the efficiency of medical …
Recently, deep learning has significantly contributed to improving the efficiency of medical …
MSN-Net: A multi-scale context nested U-Net for liver segmentation
T Fan, G Wang, X Wang, Y Li, H Wang - Signal, Image and Video …, 2021 - Springer
Liver segmentation is critical for the location and diagnosis of liver cancer. The variant of U-
Net network with skip connections has become popular in the medical image segmentation …
Net network with skip connections has become popular in the medical image segmentation …
Weakly supervised liver tumor segmentation using couinaud segment annotation
Automatic liver tumor segmentation is of great importance for assisting doctors in liver
cancer diagnosis and treatment planning. Recently, deep learning approaches trained with …
cancer diagnosis and treatment planning. Recently, deep learning approaches trained with …