Self-supervised learning for out-of-distribution detection in medical applications

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim

Abstract

Although recent neural networks have achieved great successes when the training and the testing data are sampled from the same distribution, in real-world applications, it is unnatural to control the test data distribution. Therefore, it is important for neural networks to be aware of uncertainty when new kinds of inputs (which is called out-of-distribution) are given. In this project, we consider the problem of out-of-distribution detection in neural networks. In particular, we will develop a novel self-supervised learning approach for out-of-distribution detection in medical applications.

Requirements:

• Good understanding of statistics and machine learning methods.
• Very good programming skills in Python & TensorFlow^? / PyTorch^?

Location:

• Garching

Resultant Paper

A. Ravi, S.T. Kim, F. Pfister, F. Pfister, N. Navab Self-supervised out-of-distribution detection in brain CT scans The first two authors contributed equally. Medical Imaging meets NeurIPS? workshop (bib)