  This work tackles the automatic fine-grained slide quality assessment problem
for digitized direct smears test using the Gram staining protocol. Automatic
quality assessment can provide useful information for the pathologists and the
whole digital pathology workflow. For instance, if the system found a slide to
have a low staining quality, it could send a request to the automatic slide
preparation system to remake the slide. If the system detects severe damage in
the slides, it could notify the experts that manual microscope reading may be
required. In order to address the quality assessment problem, we propose a deep
neural network based framework to automatically assess the slide quality in a
semantic way. Specifically, the first step of our framework is to perform dense
fine-grained region classification on the whole slide and calculate the region
distribution histogram. Next, our framework will generate assessments of the
slide quality from various perspectives: staining quality, information density,
damage level and which regions are more valuable for subsequent
high-magnification analysis. To make the information more accessible, we
present our results in the form of a heat map and text summaries. Additionally,
in order to stimulate research in this direction, we propose a novel dataset
for slide quality assessment. Experiments show that the proposed framework
outperforms recent related works.
