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2020 | OriginalPaper | Buchkapitel

What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification?

verfasst von : Amitojdeep Singh, Sourya Sengupta, Jothi Balaji J., Abdul Rasheed Mohammed, Ibrahim Faruq, Varadharajan Jayakumar, John Zelek, Vasudevan Lakshminarayanan

Erschienen in: Ophthalmic Medical Image Analysis

Verlag: Springer International Publishing

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Abstract

Deep learning methods for ophthalmic diagnosis have shown success for tasks like segmentation and classification but their implementation in the clinical setting is limited by the black-box nature of the algorithms. Very few studies have explored the explainability of deep learning in this domain. Attribution methods explain the decisions by assigning a relevance score to each input feature. Here, we present a comparative analysis of multiple attribution methods to explain the decisions of a convolutional neural network (CNN) in retinal disease classification from OCT images. This is the first such study to perform both quantitative and qualitative analyses. The former was performed using robustness, runtime, and sensitivity while the latter was done by a panel of eye care clinicians who rated the methods based on their correlation with diagnostic features. The study emphasizes the need for developing explainable models that address the end-user requirements, hence increasing the clinical acceptance of deep learning.

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Titel: What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification?
verfasst von: Amitojdeep Singh
Sourya Sengupta
Jothi Balaji J.
Abdul Rasheed Mohammed
Ibrahim Faruq
Varadharajan Jayakumar
John Zelek
Vasudevan Lakshminarayanan
Verlag: Springer International Publishing
Buch: Ophthalmic Medical Image Analysis
Print ISBN: 978-3-030-63418-6

Electronic ISBN: 978-3-030-63419-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-63419-3_3

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