Bio

Clinical Focus


  • Diagnostic Radiology

Academic Appointments


  • Clinical Professor, Radiology

Professional Education


  • Fellowship: UCSF Radiology Fellowships (1997) CA
  • Board Certification: Diagnostic Radiology, American Board of Radiology (1996)
  • Residency: UCLA Radiology Residency (1996) CA
  • Internship: California Pacific Medical Center Internal Medicine Residency (1992) CA
  • Medical Education: University of California at San Francisco School of Medicine (1991) CA

Publications

All Publications


  • Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images. Current biology : CB Frank, S. M., Qi, A., Ravasio, D., Sasaki, Y., Rosen, E. L., Watanabe, T. 2020

    Abstract

    There have been long-standing debates regarding whether supervised or unsupervised learning mechanisms are involved in visual perceptual learning (VPL) [1-14]. However, these debates have been based on the effects of simple feedback only about response accuracy in detection or discrimination tasks of low-level visual features such as orientation [15-22]. Here, we examined whether the content of response feedback plays a critical role for the acquisition and long-term retention of VPL of complex natural images. We trained three groups of human subjects (n= 72 in total) to better detect "grouped microcalcifications" or "architectural distortion" lesions (referred to as calcification and distortion in the following) in mammograms either with no trial-by-trial feedback, partial trial-by-trial feedback (response correctness only), or detailed trial-by-trial feedback (response correctness and target location). Distortion lesions consist of more complex visual structures than calcification lesions [23-26]. We found that partial feedback is necessary for VPL of calcifications, whereas detailed feedback is required for VPL of distortions. Furthermore, detailed feedback during training is necessary for VPL of distortion and calcification lesions to be retained for 6months. These results show that although supervised learning is heavily involved in VPL of complex natural images, the extent of supervision for VPL varies across different types of complex natural images. Such differential requirements for VPL to improve the detectability of lesions in mammograms are potentially informative for the professional training of radiologists.

    View details for DOI 10.1016/j.cub.2020.05.050

    View details for PubMedID 32502415

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