Herein, we aim to resurge morphology-based diagnosis for hematological malignancies by overcoming current limitations through the establishment of an automated procedure integrating imaging flow cytometry and deep learning to achieve objective, ultra-high throughput and sensitive diagnosis. Specifically, imaging flow cytometry allows for imaging of single-cell morphology of hundreds of thousands of peripheral blood cells that in turn enables us to learn characteristic morphologies indicative of the presence of the disease. We focus on Sézary Syndrome, an aggressive cutaneous T cell lymphoma that is characterized by presence of tumor T cells with abnormal nucleus morphology in the peripheral blood.
