Abstract
Hyperspectral imaging (HSI) is an advanced imaging technique that captures light across the magnetic spectrum in a two-dimensional space. Unlike conventional RGB-cameras, which cover only three bands (red, green and blue) of visible light, hyperspectral imagers capture hundreds of narrow, contiguous bands ranging from ultraviolet to infrared and thus beyond our visual range. The additional spectral signature makes HSI a highly sought-after technology for identification of various kinds of material. While hyperspectral imaging (HSI) has well-established applications in fields such as remote sensing, agriculture and environmental monitoring, there has been a surge of research interest from the medical field as well. This interest is not only motivated by HSI's rich spectral information, but also by its harmless nature, as opposed to for example state-of-the-art fluorescent microscopy, which might cause some side effects. The belief is that HSI holds great potential to advance surgical guidance for gastroenterology and neuro-oncology but also has a value for pathology, and many other subfields of medicine.
This doctoral trajectory will focus on the design of efficient representation learning methods for analysis of hyperspectral images extracted from medical applications. This will be complemented by a general approach for interpretability of different hyperspectral bands for these medical tasks and link this with biological meaningful processes. Based on these insights, a general framework can be developed to identify the most relevant bands related to a certain medical task, considering domain knowledge of sensor design.
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