ArticleSensors (Basel, Switzerland)2021
Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review.Digestion · 2025Pooled it
- LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.Abdominal radiology (New York) · 2026Review
- Deep learning prediction of chemo-immunotherapy response using tumor perfusion ultrasound images.Frontiers in artificial intelligence · 2026Article
- Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Joint Pre-Trained Fine-Tuning and Contrastive Learning for Contrast-Enhanced Ultrasound.Bioengineering (Basel, Switzerland) · 2025Article
- Images Versus Videos in Contrast-Enhanced Ultrasound for Computer-Aided Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Research on ultrasound-based radiomics: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Ultrasonography of Hepatocellular Carcinoma: From Diagnosis to Prognosis.Journal of clinical and translational hepatology · 2024Review
- Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning.BMC medical imaging · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnosis of Salivary Gland Tumors Using Transfer Learning with Fine-Tuning and Gradual Unfreezing.Diagnostics (Basel, Switzerland) · 2023Article
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- Review
- Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2022Review
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- Artificial intelligence in liver ultrasound.World journal of gastroenterology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.