Handling Imbalanced Data Problem in Chest X-ray Multi-label Classification

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

Abstract

Chest radiography is the most common imaging examination for screening and diagnosis of chest disease. Predicting the presence of chest radiographic observations is important in the screening of chest disease. It has challenging to train deep neural networks on ChestXray^? images due to the class imbalance problem. In this guided research project, we explore an effective training method to deal with the class imbalance in multi-label classification for training deep neural networks in chest X-ray images.

Requirements:

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

Location:

Garching

ProjectForm
Title:	Handling Imbalanced Data Problem in Chest X-ray Multi-label Classification
Abstract:	Chest radiography is the most common imaging examination for screening and diagnosis of chest disease. Predicting the presence of chest radiographic observations is important in the screening of chest disease. It has challenging to train deep neural networks on ChestXray^? images due to the class imbalance problem. In this guided research project, we explore an effective training method to deal with the class imbalance in multi-label classification for training deep neural networks in chest X-ray images.
Student:
Director:	Prof. Dr. Nassir Navab
Supervisor:	Dr. Seong Tae Kim, Ashkan Khakzar
Type:	Project
Area:	Machine Learning, Medical Imaging
Status:	finished
Start:
Finish:
Thesis (optional):
Picture:

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