There have been several situations where machine learning classifiers, trained to diagnose a particular disease (for example, lung cancer from chest x-rays), overfit on hidden features within the data. Examples include gridlines, surgical markers or evidence of treatment or text present in the images (see references for examples). This causes the classifier to fail on other type of images. …
Supervisors: Veronika Cheplygina
Semester: Spring 2023
Tags: machine learning, data science, medical imaging

GANs have been proposed for generation of synthetic cell image data [1], or in other words data augmentation. We want to perform a critical survey of the field of GANs as used for data augmentation and examine some alternatives. We believe that the notion that GANs are generating “new” data should be challenged; it in fact generates a variation of the data it is being fed for training …
Supervisors: Veronika Cheplygina
Semester: Spring 2023
Tags: machine learning, data science, medical imaging

Machine learning challenges hosted on platforms such as Kaggle (general) or grand-challenge.org (medical imaging) have attracted a lot of attention, both from academia and industry researchers. Challenge designs vary widely [1], including what type of data is available, how the algorithms are evaluated, and the rewards for the winners. In medical imaging, there is some evidence that challenges …
Supervisors: Veronika Cheplygina
Semester: Spring 2022
Tags: machine learning, data science, medical imaging

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data