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Connection

Feng Li to Diagnosis, Differential

This is a "connection" page, showing publications Feng Li has written about Diagnosis, Differential.
Connection Strength

0.285
  1. True detection versus "accidental" detection of small lung cancer by a computer-aided detection (CAD) program on chest radiographs. J Digit Imaging. 2010 Feb; 23(1):66-72.
    View in: PubMed
    Score: 0.063
  2. Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT. Acad Radiol. 2006 Aug; 13(8):943-50.
    View in: PubMed
    Score: 0.052
  3. 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: 0.046
  4. 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: 0.046
  5. Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With ThatĀ of Radiologists. Chest. 2019 07; 156(1):112-119.
    View in: PubMed
    Score: 0.031
  6. 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: 0.013
  7. Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1). Acad Radiol. 2005 Mar; 12(3):337-46.
    View in: PubMed
    Score: 0.012
  8. 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: 0.011
  9. Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys. 2003 Sep; 30(9):2440-54.
    View in: PubMed
    Score: 0.011
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.