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Artificial intelligence and especially neural networks have long been criticized for their 'black-box' nature. This was exemplified by previously surprising results of a neural network being able to classify the sex of a person based on a retina picture. We showed that we were able to extract some of the distinguishing features by thoroughly analysing the neural network. (Apr 06, 2020)
We were able to achieve expert level accuracy in solving a multiclass flow cytometry problem.This was possible by transforming the problem into self organizing maps allowing us to train a deep convolutional neural network to perform the task. The software of our classifier is open source and we make our entire model publicly accessible via https://github.com/xiamaz/flowCat. A ready-to-use webservice is available at https://hema.to. (Apr 06, 2020)