Leukemia defines a large group of hematopoietic (blood) tumors characterized by the proliferation of immature cells (called "blasts") because of alterations occurring at a certain level of the hematopoietic maturation in the bone marrow. Myeloid leukemias mainly affects myeloid cells, precursors of blood cells monocytes, granulocytes, megakaryocytes, and erythrocytes; lymphoid leukemias mainly affect lymphoid cells, precursors of lymphocytes . Acute myeloid leukemias (AMLs) and acute lymphoblastic leukemias (ALLs) represent around 40 % of all leukemia diagnoses and contain more than 35 subtypes, associated with different prognoses and treatments. Even if the definitive diagnosis may be supported by immunophenotypic and genetic analyses, the visual morphological assessment of bone marrow (BM) and peripheral blood (PB) smears is still a fundamental step . Hematologists have to count and distinguish blasts and other cell subpopulations found in BM or PB smears after manual counting of 500 and/or 200 nucleated cells, respectively . To this aim, clinicians are usually asked to recognize specific cellular types (e.g., promyelocytes, myeloid precursors, erythroid precursors, and abnormal cells) according to morphological criteria including nuclear-cytoplasmic ratio, cytoplasm granularity (i.e., presence of subcellular organelles), degree of "basophilic" features etc. This process is subjective, time-consuming, and suffers from intra-and inter-observer variability 4 , especially in case of cells belonging to the same lineage or with similar maturation stages. Therefore, there is an urgent need to provide clinicians with objective, automated and accurate approaches for leukemia assessment. In this context, many efforts have been made, including image-based automated methods coupled with machine-learning approaches 5-7 but these rely on stained samples, implying the same labor-intensive steps and scarce information, based only on morphological criteria.