MaOOD

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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

ProjectForm
Title: Self-supervised learning for out-of-distribution detection in medical applications
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.
Student: Abinav Ravi Venkatakrishnan
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim
Type: Master Thesis
Area: Machine Learning, Medical Imaging
Status: running
Start:  
Finish:  
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.2 - 13 Jan 2020 - 15:12 - SeongTaeKim