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Computational Hematopathology for Improved Diagnostics

Project description

Artificial intelligence is enhancing the interpretation of digital slides

Haematological malignancies, such as leukaemia and lymphoma, affect millions of adults and children every year, often with lethal consequences. Diagnosis relies on cellular abnormalities, and cytologists are specially trained to detect diseases from the single cell composition of blood, bone marrow and lymphoid tissues. After 150 years of research into blood diseases, clinicians still rely on their eyes for interpretation of the histopathology slides. The EU-funded CompHematoPathology project is bringing the power of artificial intelligence and mathematical modelling to the diagnoses of haematological malignancies. Using these tools and expertly annotated image data, CompHematoPathology plans to develop a data-driven model to predict blood dynamics in health and disease. It promises to enhance throughput, improve diagnoses and ultimately improve the treatment of patients with haematological malignancies.


Identifying hematologic malignancies still relies on the time-consuming and subjective visual assessment of images. Every day, cytologists and pathologists are confronted with rare diagnostic cells, ever-increasing image data, and heterogeneous disease manifestations. Although we understand blood better than any other human tissue, we are unable to quantitatively predict a patient’s blood dynamics from a measurement. Diagnosis thus depends on rough staging schemes and the expertise and intuition of the clinician.
In my proposal, I address these challenges by establishing computational hematopathology, a combination of artificial intelligence algorithms and mathematical models that will boost the currently prevailing manual assessment. Based on my experience in using these methods for scrutinizing stem cell differentiation I will combine the power of deep learning and mathematical modeling with digitized and expertly annotated image data. My unique approach enables me to design and parametrize a data-driven model to predict hematopoietic dynamics in health and disease. Since the interpretation of digitized slides is becoming the clinical standard, novel algorithms for standardized disease classification and improved diagnosis are critically needed now.
This interdisciplinary project merges methods from digital pathology, machine learning, image processing, and mathematical modeling. ComHematoPathology will provide novel approaches and software tools for automated classification of hematopathology image data, allowing for reproducible and precise diagnosis at an unprecedented level. This will increase throughput and standardize the diagnosis of blood diseases and will thus improve the treatment of patients suffering from hematologic malignancies.

Host institution

Net EU contribution
€ 1 981 213,00
85764 Neuherberg

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Bayern Oberbayern München, Landkreis
Activity type
Research Organisations
Total cost
€ 1 981 213,00

Beneficiaries (1)