Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
[HTML][HTML] Deep learning for image-based liver analysis—A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully
convolutional networks are now widely used in the medical image analysis domain. The …
convolutional networks are now widely used in the medical image analysis domain. The …
Clip-driven universal model for organ segmentation and tumor detection
An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …
segmentation and tumor detection. However, due to the small size and partially labeled …
[HTML][HTML] The liver tumor segmentation benchmark (lits)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …
(LiTS), which was organized in conjunction with the IEEE International Symposium on …
H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …
[HTML][HTML] RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their
heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks …
heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks …
Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields
Automatic segmentation of the liver and its lesion is an important step towards deriving
quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support …
quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support …
Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks
Automatic segmentation of the liver and hepatic lesions is an important step towards
deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision …
deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision …
Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs
C Sun, S Guo, H Zhang, J Li, M Chen, S Ma… - Artificial intelligence in …, 2017 - Elsevier
This paper presents a novel, fully automatic approach based on a fully convolutional
network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a …
network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a …
Ahcnet: An application of attention mechanism and hybrid connection for liver tumor segmentation in ct volumes
H Jiang, T Shi, Z Bai, L Huang - Ieee Access, 2019 - ieeexplore.ieee.org
The liver is a common site for the development of primary (ie, originating from the liver, eg,
hepatocellular carcinoma) or secondary (ie, spread to the liver, eg, colorectal cancer) tumor …
hepatocellular carcinoma) or secondary (ie, spread to the liver, eg, colorectal cancer) tumor …