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Cancer is one of the most common causes of death. Cytopathological, i.e., cell-based, diagnosis of cancer can be applied in screening scenarios and allows an early and highly sensitive detection of cancer, thus increasing the chance for cure. The detection of cancer on cells addressed in this paper is based on bright field light microscopy. The cells are imaged with a camera mounted on a microscope, allowing to measure cell properties. However, these cameras exhibit only a limited dynamic range, which often makes the quantification of properties difficult or even impossible. Consequently, to allow a computer-assisted analysis of microscopy images, the imaging has to be improved. To this end, we show how the dynamic range can be increased by acquiring a set of differently exposed cell images. These high dynamic range (HDR) images allow to measure cellular features that are otherwise difficult to capture, if at all. We show that HDR microscopy not only increases the dynamic range, but furthermore reduces noise and improves the acquisition of colors. We develop HDR microscopy-based algorithms, which are essential for cytopathological oncology and early cancer detection and only possible with HDR microscopy imaging. We show the detection of certain subcellular features, so-called AgNORs, in silver (Ag) stained specimens. Furthermore, we give examples of two further applications, namely: 1) the detection of stained cells in immunocytochemical preparations and 2) color separation for nuclear segmentation of specimens stained with low contrast.