Image processing algorithms in pathology commonly include automated decision points such as classifications. While
this enables efficient automation, there is also a risk that errors are induced. A different paradigm is to use image
processing for enhancements without introducing explicit classifications. Such enhancements can help pathologists to
increase efficiency without sacrificing accuracy. In our work, this paradigm has been applied to Ki-67 hot spot detection.
Ki-67 scoring is a routine analysis to quantify the proliferation rate of tumor cells. Cell counting in the hot spot, the
region of highest concentration of positive tumor cells, is a method increasingly used in clinical routine. An obstacle for
this method is that while hot spot selection is a task suitable for low magnification, high magnification is needed to
discern positive nuclei, thus the pathologist must perform many zooming operations. We propose to address this issue by
an image processing method that increases the visibility of the positive nuclei at low magnification levels. This tool
displays the modified version at low magnification, while gradually blending into the original image at high
magnification. The tool was evaluated in a feasibility study with four pathologists targeting routine clinical use. In a task
to compare hot spot concentrations, the average accuracy was 75±4.1% using the tool and 69±4.6% without it (n=4). Feedback on the system, gathered from an observer study, indicate that the pathologists found the tool useful and fitting in their existing diagnostic process. The pathologists judged the tool to be feasible for implementation in clinical routine.