Blood morphology and image-based artificial intelligence 

Research projects
  • 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.