Search Results for author: Shazia Dharssi

Found 3 papers, 2 papers with code

A deep learning approach for automated detection of geographic atrophy from color fundus photographs

1 code implementation7 Jun 2019 Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng, Qingyu Chen, Elvira Agrón, Wai T. Wong, Zhiyong Lu, Emily Y. Chew

Results: The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUC of 0. 933-0. 976, 0. 939-0. 976, and 0. 827-0. 888, respectively.

Specificity

DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs

1 code implementation19 Nov 2018 Yifan Peng, Shazia Dharssi, Qingyu Chen, Tiarnan D. Keenan, Elvira Agrón, Wai T. Wong, Emily Y. Chew, Zhiyong Lu

DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale.

Decision Making General Classification

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