Junji Shiraishi to Lung Neoplasms
This is a "connection" page, showing publications Junji Shiraishi has written about Lung Neoplasms.
Connection Strength
1.169
-
[Radiomics for Estimating Recurrence Risk of Patients with Lung Cancer by Using Survival Analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2021; 77(2):153-159.
Score: 0.321
-
Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs. Acad Radiol. 2007 Jan; 14(1):28-37.
Score: 0.122
-
Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance. Acad Radiol. 2006 Aug; 13(8):995-1003.
Score: 0.118
-
Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med Phys. 2006 Jul; 33(7):2642-53.
Score: 0.118
-
Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience. Radiology. 2003 May; 227(2):469-74.
Score: 0.094
-
Temporal subtraction method for lung nodule detection on successive thoracic CT soft-copy images. Radiology. 2014 Apr; 271(1):255-61.
Score: 0.049
-
Ethnic difference in hematological toxicity in patients with non-small cell lung cancer treated with chemotherapy: a pooled analysis on Asian versus non-Asian in phase II and III clinical trials. J Thorac Oncol. 2011 Nov; 6(11):1881-8.
Score: 0.043
-
Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization. AJR Am J Roentgenol. 2011 May; 196(5):W535-41.
Score: 0.041
-
Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy. Acad Radiol. 2008 Dec; 15(12):1505-12.
Score: 0.035
-
Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. AJR Am J Roentgenol. 2008 Jul; 191(1):260-5.
Score: 0.034
-
Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology. 2005 Nov; 237(2):684-90.
Score: 0.028
-
Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT MR. 2005 Oct; 26(5):357-63.
Score: 0.028
-
[Status of radiological screening in the USA]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2005 Jun 20; 61(6):745-8.
Score: 0.027
-
False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol. 2005 Feb; 12(2):191-201.
Score: 0.027
-
Effect of temporal subtraction images on radiologists' detection of lung cancer on CT: results of the observer performance study with use of film computed tomography images. Acad Radiol. 2004 Dec; 11(12):1337-43.
Score: 0.026
-
Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR Am J Roentgenol. 2004 Nov; 183(5):1209-15.
Score: 0.026
-
Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol. 2004 Jun; 11(6):617-29.
Score: 0.025
-
Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med Phys. 2003 Oct; 30(10):2584-93.
Score: 0.006