Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.A...Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.Artificial intelligence(AI),such as machine learning(ML)and deep learning,has recently gained attention for its capability to reveal quantitative information on images.Currently,AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine.This review summarizes the applications of AI in various aspects of preoperative imaging of HCC,including segmentation,differential diagnosis,prediction of histo-pathology,early detection of recurrence after curative treatment,and evaluation of treatment response.We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.展开更多
We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum de...We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.展开更多
基金CAMS Innovation Fund for Medical Sciences(CIFMS),No.2016-I2M-1-001PUMC Youth Fund,No.2017320010+2 种基金Chinese Academy of Medical Sciences(CAMS)Research Fund,No.ZZ2016B01Beijing HopeRun Special Fund of Cancer Foundation of China,No.LC2016B15PUMC Postgraduate Education and Teaching Reform Fund,No.10023201900303.
文摘Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.Artificial intelligence(AI),such as machine learning(ML)and deep learning,has recently gained attention for its capability to reveal quantitative information on images.Currently,AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine.This review summarizes the applications of AI in various aspects of preoperative imaging of HCC,including segmentation,differential diagnosis,prediction of histo-pathology,early detection of recurrence after curative treatment,and evaluation of treatment response.We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.
基金Project supported by the National Natural Science Foundation of China(Nos.60973059 and 81171407)the Program for New Century Excellent Talents in University,China(No.NCET-10-0044)
文摘We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.
