Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

[1] Chartrand G(2017)Deep learning: a primer for radiologists Radiographics 37 2113-2131

[2] Cheng PM(2016)Lung pattern classification for interstitial lung diseases using a deep convolutional neural network IEEE Trans Med Imaging 35 1207-1216

[3] Vorontsov E(2016)Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique IEEE Trans Med Imaging 35 1153-1159

[4] Anthimopoulos M(2002)Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks Ecol Model 154 135-150

[5] Christodoulidis S(1996)Beyond the black box: open implementation IEEE Softw 13 10-11

[6] Ebner L(2001)Artificial neural networks: opening the black box Cancer 91 1615-1635

[7] Christe A(2018)Nonstandardized terminology to describe focal liver lesions in patients at risk for hepatocellular carcinoma: implications regarding clinical communication AJR Am J Roentgenol 210 85-90

[8] Mougiakakou S(2015)LI-RADS (Liver Imaging Reporting and Data System): summary, discussion, and consensus of the LI-RADS Management Working Group and future directions Hepatology 61 1056-1065

[9] Greenspan H(2018)Management implications and outcomes of LI-RADS-2, -3, -4, and -M category observations Abdom Radiol (NY) 43 143-148

[10] Van Ginneken B(2016)Reliability, validity, and reader acceptance of LI-RADS-an in-depth analysis Acad Radiol 23 1145-969

[1] Chartrand G(2017)Deep learning: a primer for radiologists Radiographics 37 2113-2131

[2] Cheng PM(2016)Lung pattern classification for interstitial lung diseases using a deep convolutional neural network IEEE Trans Med Imaging 35 1207-1216

[3] Vorontsov E(2016)Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique IEEE Trans Med Imaging 35 1153-1159

[4] Anthimopoulos M(2002)Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks Ecol Model 154 135-150

[5] Christodoulidis S(1996)Beyond the black box: open implementation IEEE Softw 13 10-11

[6] Ebner L(2001)Artificial neural networks: opening the black box Cancer 91 1615-1635

[7] Christe A(2018)Nonstandardized terminology to describe focal liver lesions in patients at risk for hepatocellular carcinoma: implications regarding clinical communication AJR Am J Roentgenol 210 85-90

[8] Mougiakakou S(2015)LI-RADS (Liver Imaging Reporting and Data System): summary, discussion, and consensus of the LI-RADS Management Working Group and future directions Hepatology 61 1056-1065

[9] Greenspan H(2018)Management implications and outcomes of LI-RADS-2, -3, -4, and -M category observations Abdom Radiol (NY) 43 143-148

[10] Van Ginneken B(2016)Reliability, validity, and reader acceptance of LI-RADS-an in-depth analysis Acad Radiol 23 1145-969