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- W2955669510 abstract "262Most approaches to deep learning (DL)-based medical image classification output a binary decision about the presence or absence of a disease without explicitly justifying decisions. Moreover, disease severity prediction in an unsupervised approach is not clearly defined since the absence of disease labels prevents validation of decisions, as in diabetic retinopathy (Son et al., 2020). Diseases such as age-related macular degeneration (AMD) do not have a standard clinical severity scale, and it is left to the observer’s expertise to assess severity. While class activation maps (CAMs) (Zhou et al., 2016) highlight image regions that have high response to the trained classifier, they do not provide measurable parameters to explain the decision. Explainability of classifier decisions is an essential requirement of modern diagnosis systems." @default.
- W2955669510 created "2019-07-12" @default.
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- W2955669510 date "2020-12-02" @default.
- W2955669510 modified "2023-10-01" @default.
- W2955669510 title "AMD Severity Prediction and Explainability Using Image Registration and Deep Embedded Clustering" @default.
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