Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis

@article{Singh2020EvaluationOE,
  title={Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis},
  author={Amitojdeep Singh and J. Jothi Balaji and Varadharajan Jayakumar and Mohammed Abdul Rasheed and Rajiv Raman and Vasudevan Lakshminarayanan},
  journal={Clinical Ophthalmology (Auckland, N.Z.)},
  year={2020},
  volume={15},
  pages={2573 - 2581}
}
Background The lack of explanations for the decisions made by deep learning algorithms has hampered their acceptance by the clinical community despite highly accurate results on multiple problems. Attribution methods explaining deep learning models have been tested on medical imaging problems. The performance of various attribution methods has been compared for models trained on standard machine learning datasets but not on medical images. In this study, we performed a comparative analysis to… 

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