AI-based processing of bone-marrow smears to support leukaemia diagnosis. Using so-called unsupervised learning methods, single-cell images (centre) are extracted from extremely high-resolution image data (left). Then, using neural networks, these cell images are examined to check for any visual anomalies which might have a genetic origin. Areas which are important for the decision of the neural network are highlighted in colour by using so-called ’explainable AI’ strategies (right).
IT specialists and physicians develop new method for recognising genetic aberrations. AI-based processing of bone-marrow smears to support leukaemia diagnosis. Using so-called unsupervised learning methods, single-cell images ( centre ) are extracted from extremely high-resolution image data ( left ). Then, using neural networks, these cell images are examined to check for any visual anomalies which might have a genetic origin. Areas which are important for the decision of the neural network are highlighted in colour by using so-called 'explainable AI' strategies ( right ). AG Risse Decisions on treatment for patients with acute myeloid leukemia (AML) - a highly aggressive form of leukemia - are based, among other things, on a series of certain genetic features of the disease; but at the time when a diagnosis is made, this information is not available. However, evidence of these genetic anomalies is crucial in providing targeted treatment for patients at an early stage.
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