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Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis

CANCERS (2023)

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Shuo Zhang et al.

Summary: Semi-supervised learning is powerful for medical image segmentation with limited labeled data, but most approaches focus on single-modal data. Our proposed Semi-CML framework leverages the advantages of multi-modal data to improve segmentation performance. However, the need for multi-modal data in both training and inference stages limits its practical usage. To address this, we introduce the ASC loss and the PReL module, which significantly outperform state-of-the-art methods and achieve comparable performance to fully supervised methods while reducing annotation costs by 90%.

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2023)

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An automated liver tumour segmentation and classification model by deep learning based approaches

Sayan Saha Roy et al.

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Anh-Cang Phan et al.

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Deep feature fusion and optimized feature selection based ensemble classification of liver lesions

A. Anisha et al.

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Summary: Imaging characteristics of P53-mutated hepatocellular carcinoma (HCC) can be determined using dynamic CT and gadoxetic acid-enhanced MRI, and they are correlated with clinical features, pathologic findings, and prognosis. Dilated vasculature at the arterial phase of dynamic CT and a lower relative enhancement ratio at the hepatobiliary phase of gadoxetic acid-enhanced MRI are useful markers for predicting P53-mutated HCC.

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Xuehu Wang et al.

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Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques

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Summary: Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor and a major cause of cancer-related deaths worldwide. Noninvasive and accurate HCC detection based on medical images is being achieved through computerized methods. Our research combines conventional texture analysis with advanced classifiers and deep learning techniques such as Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE) to automatically diagnose HCC. The best accuracy of 91% was obtained for B-mode ultrasound images using CNN.

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Abhishek Midya et al.

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2023)

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Multi-input dense convolutional network for classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

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Diagnosis of hepatocellular carcinoma using deep network with multi-view enhanced patterns mined in contrast-enhanced ultrasound data

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Wei-bin Zhang et al.

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Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Jian Zhang et al.

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Summary: UK to start COVID-19 vaccine rollout, prioritizing high-risk individuals.

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Arndt Vogel et al.

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Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

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Summary: The systematic review analyzed articles on the application of artificial intelligence in HCC detection and characterization, showing a constant improvement in publication quality over time. Different AI methods were mainly applied in CT, US, and MRI, indicating the reliability and advancement of medical imaging technology in HCC diagnosis.

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NATURE REVIEWS DISEASE PRIMERS (2021)

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Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data

Ruitian Gao et al.

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In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Wen-Fan Chen et al.

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung et al.

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CA-A CANCER JOURNAL FOR CLINICIANS (2021)

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EUROPEAN RADIOLOGY (2021)

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