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Co-Authors

This is a "connection" page, showing publications co-authored by Hiroyuki Abe and Kunio Doi.

 
Connection Strength
 
 
 
2.808
 
  1. Abe H, Ishida T, Shiraishi J, Li F, Katsuragawa S, Sone S, Macmahon H, Doi K. 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.
    View in: PubMed
    Score: 0.308
  2. Abe H, Macmahon H, Shiraishi J, Li Q, Engelmann R, Doi K. Computer-aided diagnosis in chest radiology. Semin Ultrasound CT MR. 2004 Oct; 25(5):432-7.
    View in: PubMed
    Score: 0.304
  3. Abe H, Ashizawa K, Li F, Matsuyama N, Fukushima A, Shiraishi J, MacMahon H, Doi K. Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases. Acad Radiol. 2004 Jan; 11(1):29-37.
    View in: PubMed
    Score: 0.289
  4. Abe H, MacMahon H, Engelmann R, Li Q, Shiraishi J, Katsuragawa S, Aoyama M, Ishida T, Ashizawa K, Metz CE, Doi K. Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies. Radiographics. 2003 Jan-Feb; 23(1):255-65.
    View in: PubMed
    Score: 0.270
  5. Abe H, Ashizawa K, Katsuragawa S, MacMahon H, Doi K. Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs. Acad Radiol. 2002 Jan; 9(1):13-7.
    View in: PubMed
    Score: 0.252
  6. Nakayama R, Abe H, Shiraishi J, Doi K. Evaluation of objective similarity measures for selecting similar images of mammographic lesions. J Digit Imaging. 2011 Feb; 24(1):75-85.
    View in: PubMed
    Score: 0.118
  7. Nakayama R, Abe H, Shiraishi J, Doi K. Potential usefulness of similar images in the differential diagnosis of clustered microcalcifications on mammograms. Radiology. 2009 Dec; 253(3):625-31.
    View in: PubMed
    Score: 0.108
  8. Shiraishi J, Abe H, Ichikawa K, Schmidt RA, Doi K. Observer study for evaluating potential utility of a super-high-resolution LCD in the detection of clustered microcalcifications on digital mammograms. J Digit Imaging. 2010 Apr; 23(2):161-9.
    View in: PubMed
    Score: 0.104
  9. Shiraishi J, Abe H, Li F, Engelmann R, MacMahon H, Doi K. 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.
    View in: PubMed
    Score: 0.086
  10. Suzuki K, Abe H, MacMahon H, Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging. 2006 Apr; 25(4):406-16.
    View in: PubMed
    Score: 0.084
  11. Li F, Arimura H, Suzuki K, Shiraishi J, Li Q, Abe H, Engelmann R, Sone S, MacMahon H, Doi K. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology. 2005 Nov; 237(2):684-90.
    View in: PubMed
    Score: 0.082
  12. Li Q, Li F, Suzuki K, Shiraishi J, Abe H, Engelmann R, Nie Y, MacMahon H, Doi K. Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT MR. 2005 Oct; 26(5):357-63.
    View in: PubMed
    Score: 0.082
  13. Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K. 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.
    View in: PubMed
    Score: 0.078
  14. Li F, Aoyama M, Shiraishi J, Abe H, Li Q, Suzuki K, Engelmann R, Sone S, Macmahon H, Doi K. 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.
    View in: PubMed
    Score: 0.077
  15. Li F, Sone S, Abe H, Macmahon H, Doi K. Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. Radiology. 2004 Dec; 233(3):793-8.
    View in: PubMed
    Score: 0.076
  16. Arimura H, Li Q, Korogi Y, Hirai T, Abe H, Yamashita Y, Katsuragawa S, Ikeda R, Doi K. Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional magnetic resonance angiography. Acad Radiol. 2004 Oct; 11(10):1093-104.
    View in: PubMed
    Score: 0.076
  17. Shiraishi J, Abe H, Engelmann R, Doi K. Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: observer performance study. Acad Radiol. 2003 Nov; 10(11):1302-11.
    View in: PubMed
    Score: 0.071
  18. Li F, Sone S, Abe H, MacMahon H, Doi K. Low-dose computed tomography screening for lung cancer in a general population: characteristics of cancer in non-smokers versus smokers. Acad Radiol. 2003 Sep; 10(9):1013-20.
    View in: PubMed
    Score: 0.071
  19. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003 Sep; 30(9):2440-54.
    View in: PubMed
    Score: 0.071
  20. Shiraishi J, Abe H, Engelmann R, Aoyama M, MacMahon H, Doi K. 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.
    View in: PubMed
    Score: 0.069
  21. Li F, Sone S, Abe H, MacMahon H, Armato SG, Doi K. Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology. 2002 Dec; 225(3):673-83.
    View in: PubMed
    Score: 0.067
  22. Shimosegawa M, Aoyama M, Li Q, Abe H, Li F, Shiraishi J, Doi K. [Development of an image processing scheme for chest radiographs using a dot printer]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2002 Sep; 58(9):1268-77.
    View in: PubMed
    Score: 0.066
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.