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GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported.
Train, Learn, Expand, Repeat
High-quality labeled data is essential to successfully train supervised machine learning models.
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