Farah Deeba

Farah Deeba

Farah Deeba (S’15) received the B.Sc. degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in 2013, and the M.Sc. degree in Electrical and Computer Engineering from the University of Saskatchewan, Canada in 2016. She is currently a Ph.D. student in Electrical Engineering department at the University of British Columbia, Vancouver, Canada. Her research interest includes medical image analysis, computer-aided detection system design, computer vision, and machine learning.


Contributions

  • Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion
    Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion

    The detection of non-polypoid superficial neoplastic lesions using current standard of white light endoscopy surveillance and random biopsy is associated with high miss rate. The subtle changes in mucosa caused by the flat and depressed neoplasms often go undetected and do not qualify for further investigation, e.g., biopsy and resection, thus increasing the risk of cancer advancement. This paper presents a screening tool named the saliency-aided visual enhancement (SAVE) method, with an objective of highlighting abnormalities in endoscopic images to detect early lesions.

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