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Artificial Intelligence in Oncology - Supporting scientific research
In short, Johannes Textor's research entails the following:
'Salivary gland cancer is a rare type of head-and-neck cancer with 150-200 diagnoses per year in the Netherlands, and the most aggressive subtypes have poor prognosis. To develop new treatment options, we are imaging the interactions between immune system cells and tumor cells within patient biopsies using high-resolution digital microscopy. Machine learning approaches are the state of the art for analyzing such data, but they can require very large datasets to train on, which are usually not available for rare cancer types. In our project, we will address this problem using "transfer learning" methodology that allows machine learning algorithms to benefit from experience gained on larger datasets from more common cancer types and train more effectively on smaller datasets. Leveraging existing data and knowledge in this manner, we hope that our project will help to build a rationale for future immunotherapy treatments for salivary gland caner patients.'
We have gathered tissue biopsies from salivary gland cancer patients in the Netherlands and Japan. We have stained these tissue biopsies with multiple fluorescent markers to detect many types of immune cells and are now analyzing digital images of these biopsies using our custom-made artificial neural network.