International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics

Comparison Study of Transformation Methods of Pet Raw Data Into Images for Classification Using Convolutional Neural Networks

15 Sep 2022, 09:40

30m

Talk Machine Learning in Medicine Machine Learning in Medical Applications 2

Speaker

Paweł Konieczka (National Centre for Nuclear Research)

Description

Convolutional Neural Networks (CNNs) have been effectively applied in many studies where crucial information about the data is embedded in the order of features (e.g. images). However, most tabular data – such as raw Positron Emission Tomography (PET) data – do not assume a spatial correlation between features, and hence are unsuitable for CNNs classification. In order to use the power of CNNs (including GPU utilization) for classification purposes of non-image data, a transformation method of 1-D vector into image has to be applied. A method comparison of transforming tabular data into input images for CNN classification will be presented. Self-organizing map and DeepInsight method were used in this study.