NMF for quality control of multi-modal retinal images for diagnosis of diabetes mellitus and diabetic retinopathy

In current ophthalmology, images of the vascular system in the human retina are used as exploratory proxies for pathologies affecting different organs. In this brief paper, we use multi-modal retinal images for assisting diagnostic decision making in diabetes mellitus and diabetic retinopathy. We report the use of matrix factorization-based source extraction techniques to pre-process the images as a data quality control step prior to their classification. Through this quality control, we unveil some relevant sources of bias in the data. After correcting for them, promising pathology classification results are still obtained, which merit further exploration. ; This research is partially funded by research grant PID2019-104551RB-I00 ; Peer Reviewed ; Article signat per 20 autors/es: Anass Benali 1,2; Laura Carrera 1,2; Ann Christin 1,2; Ruben Martín 1,2,6; Anibal Alé 3; Marina Barraso 3; Carolina Bernal 3; Sara Marín 3; Silvia Feu 3; Josep Rosinés 3; Teresa Hernandez 3,6; Irene Vilá 3,6; Cristian Oliva 3,6; Irene Vinagre 4,5,6; Emilio Ortega 4,5,6; Marga Gimenez 4,5,6; Enric Esmatjes 4,5,6; Javier Zarranz-Ventura 3,4,6; Enrique Romero 1,2 and Alfredo Vellido 1,2 // 1 Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center; 2 Computer Science Department, Facultat d’Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC BarcelonaTech); 3 Institut Clínic d’Oftalmología (ICOF), Hospital Clínic de Barcelona, Barcelona, Spain; 4 Diabetes Unit, Hospital Clínic de Barcelona, Barcelona, Spain; 5 Institut Clínic de Malalties Digestives i Metaboliques (ICMDM), Hospital Clínic de Barcelona, Barcelona, Spain; 6 August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain ; Postprint (author's final draft)