Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data
S Zhen, M Cheng, Y Tao, Y Wang… - Frontiers in …, 2020 - frontiersin.org
Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer
patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for …
patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for …
Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
Objectives To develop a proof-of-concept “interpretable” deep learning prototype that
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …
Deep learning workflow in radiology: a primer
E Montagnon, M Cerny, A Cadrin-Chênevert… - Insights into …, 2020 - Springer
Interest for deep learning in radiology has increased tremendously in the past decade due to
the high achievable performance for various computer vision tasks such as detection …
the high achievable performance for various computer vision tasks such as detection …
Deep neural architectures for contrast enhanced ultrasound (CEUS) focal liver lesions automated diagnosis
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 …
tremendous impact on the interpretation of medical images today. Among biomedical image …
Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
[HTML][HTML] Artificial intelligence in gastroenterology and hepatology: Status and challenges
Abstract Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has
achieved a breakthrough and revolutionized the processing methods of clinical medicine …
achieved a breakthrough and revolutionized the processing methods of clinical medicine …
Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework
X Dong, Y Zhou, L Wang, J Peng, Y Lou, Y Fan - IEEE Access, 2020 - ieeexplore.ieee.org
Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and
time consuming to identify the cancer tissue manually in the present scenario. The …
time consuming to identify the cancer tissue manually in the present scenario. The …
Hello world deep learning in medical imaging
There is recent popularity in applying machine learning to medical imaging, notably deep
learning, which has achieved state-of-the-art performance in image analysis and …
learning, which has achieved state-of-the-art performance in image analysis and …
[图书][B] Deep learning and convolutional neural networks for medical imaging and clinical informatics
This book is the second edition of a series documenting how deep learning and deep neural
networks are being successfully employed within medical image computing. Looking back to …
networks are being successfully employed within medical image computing. Looking back to …
Artificial intelligence in diagnostic radiology: where do we stand, challenges, and opportunities
Artificial intelligence (AI) is the most revolutionizing development in the health care industry
in the current decade, with diagnostic imaging having the greatest share in such …
in the current decade, with diagnostic imaging having the greatest share in such …