Self-supervised Learning for Tumor Microenvironment Analysis: Addressing Label Scarcity in Multiplexed Immunofluorescence Imaging with Novel Feature Extraction Techniques

The study of tumor microenvironments (TMEs) and immune cell composition in cancer, a disease characterized by uncontrolled growth and spread of tumor cells, has become increasingly important for understanding tumor progression and patient outcomes. Tools such as the TME-Analyzer enable this kind of research, but their manual workflows highlight...

Interpretable Machine Learning for Biomarker Discovery in Imaging Mass Spectrometry Data

Imaging mass spectrometry (IMS) is a multiplexed chemical imaging technique that enables the spatially targeted molecular mapping of biological samples at cellular resolutions. Within a single experiment, IMS can measure the spatial distribution and relative concentration of thousands of distinct molecular species across the surface of a tissue...