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Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Feng Li and Samuel G. Armato.
Connection Strength

2.942
  1. Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma. Eur Radiol. 2019 Jun; 29(6):2981-2988.
    View in: PubMed
    Score: 0.689
  2. Convolutional Neural Networks for Segmentation of Malignant Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance). ArXiv. 2023 Nov 30.
    View in: PubMed
    Score: 0.242
  3. Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. J Digit Imaging. 2021 08; 34(4):922-931.
    View in: PubMed
    Score: 0.206
  4. Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion. J Med Imaging (Bellingham). 2020 Jan; 7(1):012705.
    View in: PubMed
    Score: 0.185
  5. Response. Chest. 2019 10; 156(4):810-811.
    View in: PubMed
    Score: 0.181
  6. 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: 0.175
  7. Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
    View in: PubMed
    Score: 0.154
  8. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
    View in: PubMed
    Score: 0.149
  9. Clinical significance of noncalcified lung nodules in patients with breast cancer. Breast Cancer Res Treat. 2016 Sep; 159(2):265-71.
    View in: PubMed
    Score: 0.146
  10. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
    View in: PubMed
    Score: 0.133
  11. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
    View in: PubMed
    Score: 0.131
  12. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012 Dec; 22(12):2729-35.
    View in: PubMed
    Score: 0.110
  13. Research imaging in an academic medical center. Acad Radiol. 2012 Jun; 19(6):762-71.
    View in: PubMed
    Score: 0.108
  14. Dual energy subtraction and temporal subtraction chest radiography. J Thorac Imaging. 2008 May; 23(2):77-85.
    View in: PubMed
    Score: 0.082
  15. 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: 0.066
  16. 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
  17. 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: 0.056
  18. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002 Dec; 225(3):685-92.
    View in: PubMed
    Score: 0.056
  19. 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: 0.015
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.