  Traditionally, pathological analysis and diagnosis are performed by manually
eyeballing glass slide specimens under a microscope by an expert. The whole
slide image is the digital specimen produced from the glass slide. Whole slide
image enabled specimens to be observed on a computer screen and led to
computational pathology where computer vision and artificial intelligence are
utilized for automated analysis and diagnosis. With the current computational
advancement, the entire whole slide image can be analyzed autonomously without
human supervision. However, the analysis could fail or lead to wrong diagnosis
if the whole slide image is affected by tissue artifacts such as tissue fold or
air bubbles depending on the severity. Existing artifact detection methods rely
on experts for severity assessment to eliminate artifact affected regions from
the analysis. This process is time consuming, exhausting and undermines the
goal of automated analysis or removal of artifacts without evaluating their
severity, which could result in the loss of diagnostically important data.
Therefore, it is necessary to detect artifacts and then assess their severity
automatically. In this paper, we propose a system that incorporates severity
evaluation with artifact detection utilizing convolutional neural networks. The
proposed system uses DoubleUNet to segment artifacts and an ensemble network of
six fine tuned convolutional neural network models to determine severity. This
method outperformed current state of the art in accuracy by 9 percent for
artifact segmentation and achieved a strong correlation of 97 percent with the
evaluation of pathologists for severity assessment. The robustness of the
system was demonstrated using our proposed heterogeneous dataset and practical
usability was ensured by integrating it with an automated analysis system.
