The shortwave infrared (SWIR) shows a significantly higher level of contrast between different types of tissues compared to the Vis-NIR. This contrast can be further enhanced by using hyperspectral imaging (HSI). Unlike conventional imaging methods, each HSI pixel contains a full high-resolution spectral signal. Complex computational approaches are required to analyze HSI-measured optical alterations. Many of these approaches including machine learning and deep learning algorithms have been developed in the field of geospatial imaging. We adapted and optimized algorithms used in remote sensing to build an image processing platform IDCube that processes biomedical data and identifies tissues abnormalities in SWIR.
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