articleIEEE Transactions on Medical ImagingNov 6, 2019Closed access

CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

Chinese University of Hong Kong

PubMed
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Abstract

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop automatic grading methods with only image-level supervision. In this article, we present a novel cross-disease attention…

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Authors

6

Topics & keywords

Keywords
  • Grading (engineering)
  • Diabetic retinopathy
  • Computer science
  • Medicine
  • Disease
  • Artificial intelligence
  • Optometry
  • Ophthalmology
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