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Connection

Kunio Doi to Cluster Analysis

This is a "connection" page, showing publications Kunio Doi has written about Cluster Analysis.

 
Connection Strength
 
 
 
0.433
 
  1. Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doi K. Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms. Acad Radiol. 2006 Oct; 13(10):1219-28.
    View in: PubMed
    Score: 0.073
  2. Muramatsu C, Li Q, Schmidt R, Suzuki K, Shiraishi J, Newstead G, Doi K. Experimental determination of subjective similarity for pairs of clustered microcalcifications on mammograms: observer study results. Med Phys. 2006 Sep; 33(9):3460-8.
    View in: PubMed
    Score: 0.072
  3. 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: 0.070
  4. Nakayama R, Uchiyama Y, Watanabe R, Katsuragawa S, Namba K, Doi K. Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. Med Phys. 2004 Apr; 31(4):789-99.
    View in: PubMed
    Score: 0.061
  5. Suzuki K, Armato SG, Li F, Sone S, Doi K. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys. 2003 Jul; 30(7):1602-17.
    View in: PubMed
    Score: 0.058
  6. Armato SG, Altman MB, Wilkie J, Sone S, Li F, Doi K, Roy AS. Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
    View in: PubMed
    Score: 0.058
  7. Zhang W, Yoshida H, Nishikawa RM, Doi K. Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Med Phys. 1998 Jun; 25(6):949-56.
    View in: PubMed
    Score: 0.041
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.