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Carcinoma, Hepatocellular

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GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.

International journal of molecular sciences
Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circR...

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.

BMC ecology and evolution
BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation.

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

International journal of computer assisted radiology and surgery
PURPOSE: Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate dee...

Regression plane concept for analysing continuous cellular processes with machine learning.

Nature communications
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

European radiology
OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.

Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning.

Physics in medicine and biology
Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This st...

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

Journal of cancer research and clinical oncology
PURPOSE: Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies an...

Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC.

European radiology
OBJECTIVES: To develop and evaluate a deep learning-based model capable of detecting primary hepatic malignancies in multiphase CT images of patients at high risk for hepatocellular carcinoma (HCC).

An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
INTRODUCTION: Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical al...