Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression

Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, missing the finer distinction and intrinsic relation between the different possible stages or grades. Ordinal regression (or classification) considers the order of the values of the categorical labels and thus takes into account the order of grading scales used to assess the severity of different medical conditions. This paper presents a quantum-inspired deep probabilistic learning ordinal regression model for medical image diagnosis that takes advantage of the representational power of deep learning and the intrinsic ordinal information of disease stages. The method is evaluated on two different medical image analysis tasks: prostate cancer diagnosis and diabetic retinopathy grade estimation on eye fundus images. The experimental results show that the proposed method not only improves the diagnosis performance on the two tasks but also the interpretability of the results by quantifying the uncertainty of the predictions in comparison to conventional deep classification and regression architectures. The code and datasets are available at https://github.com/stoledoc/DQOR

Divulgación Científica No.3

La investigación constituye en sí misma un valioso instrumento para ser empleado por la sociedad, es mucho más que un objeto —sin abandonar lo que por naturaleza le compete: garantizar procesos de calidad—, puede entregar valiosa información sobre diferentes temas de interés, para avanzar en el análisis y propiciar la creación de redes de conocimiento.Research constitutes in itself a valuable instrument to be used by society, it is much more than an object —without abandoning what by nature is its responsibility: guaranteeing quality processes—, it can provide valuable information on different topics of interest, to advance in analysis and promote the creation of knowledge networks