[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 …
Deep federated machine learning-based optimization methods for liver tumor diagnosis: A review
Computer-aided liver diagnosis helps doctors accurately identify liver abnormalities and
reduce the risk of liver surgery. Early diagnosis and detection of liver lesions depend mainly …
reduce the risk of liver surgery. Early diagnosis and detection of liver lesions depend mainly …
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 …
A deep learning approach for liver and tumor segmentation in CT images using ResUNet
According to the most recent estimates from global cancer statistics for 2020, liver cancer is
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
Liver tumor segmentation in CT scans using modified SegNet
S Almotairi, G Kareem, M Aouf, B Almutairi… - Sensors, 2020 - mdpi.com
The main cause of death related to cancer worldwide is from hepatic cancer. Detection of
hepatic cancer early using computed tomography (CT) could prevent millions of patients' …
hepatic cancer early using computed tomography (CT) could prevent millions of patients' …
Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net
The segmentation of pancreas is important for medical image analysis, yet it faces great
challenges of class imbalance, background distractions, and non-rigid geometrical features …
challenges of class imbalance, background distractions, and non-rigid geometrical features …
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 …
Feature fusion encoder decoder network for automatic liver lesion segmentation
X Chen, R Zhang, P Yan - 2019 IEEE 16th international …, 2019 - ieeexplore.ieee.org
Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently,
deep learning based image segmentation methods have achieved promising performance …
deep learning based image segmentation methods have achieved promising performance …
Liver tumor localization based on YOLOv3 and 3D-semantic segmentation using deep neural networks
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of
computed tomography (CT) for early detection of liver cancer could save millions of lives per …
computed tomography (CT) for early detection of liver cancer could save millions of lives per …
Deep learning approach for medical image analysis
Localization of region of interest (ROI) is paramount to the analysis of medical images to
assist in the identification and detection of diseases. In this research, we explore the …
assist in the identification and detection of diseases. In this research, we explore the …