Our work on “Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression“, by S. Toledo-Cortés D. H. Useche H. Müller and F. A. González, has been published in Computers in Biology and Medicine (Elsevier).
A new end-to-end neural probabilistic ordinal regression method is proposed to predict a probability distributions over a range of grades. The model, evaluated on prostate cancer diagnosis and diabetic retinopathy grade estimation, can also quantify its uncertainty.
Overview of the proposed approach for diabetic retinopathy (DR) grading. An Inception-V3 network was used as feature extractor for the eye fundus image. These features were
the input for the regressor model, which yielded a posterior probability distribution over the DR grades.