Blood morphology and image-based artificial intelligence
Characterization and automatic classification of leukemic cells by means of digital image processing and pattern recognition to support the diagnosis. Ministerio de Economía y Competitividad. Project 2014-2019.
A methdology for automatic classification of atypical lymphoid cells from peripheral blood by means of digital image processing and machine learning. PhD Thesis by Santiago Alférez, 2012-2015.
A deep learning system for the automatic classification of normal and dysplastic blood cells as a support tool for the diaghnosis. PhD research project by Andrea Acevedo, 2016-2020.
A new segmentation framework for image analysis of malignant lymphoid cells in peripheral blood. Postdoctoral project by Santiago Alférez, 2016-18.
New quantitative features for the morphologic characterization of normal, reactive and abnormal lymphoid cells and blast cells in peripheral blood through digital imaghe analysis. PhD Thesis by Laura Puigví, 2016-2019.
Automatic classification of myeloid, monocytic, promyelocytic and lymphoid blast cells. PhD research project by Laura Boldú, 2017-2020.
Automatic detection and classification of abnormal red cells and malaria detection. PhD research project by Angel Molina, 2018-2021.
Building a data base of abnormal peripheral blood cell images containing close to 100.000
Development of an image-based blood cell recognition system for infectious diseases detection by means of machine learning techniques. Healthcare SLU. Project 2019-2021.