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hi everyone and welcome to this e-poster presentation of our bvm paper realistic evaluation of fix match on imbalanced medical image classification tasks in this project we took a look at existing works on semi-supervised learning for medical image classification and notice that some aspects are often treated with little care for example hyperparameter tuning methods are often not reported which could result in a weak supervised baseline moreover class imbalance is rarely taken into account although its ubiquitous in medical applications our goal was to explore these two points on a state-of-the-art semi-supervised learning method and for that we used the following setup experiments were performed on data sets of chest x-rays and retina images as you can see on the right both tasks include class imbalance we then trained deep neural networks on both data sets and used the fixed match algorithm for semi-supervised learning to enable a fair comparison between supervised only and semi-su