From out-of-distribution detection to quality control
Résumé
Quality Control (QC) is an important step of any medical image analysis pipeline to impose safeguards against biased interpretation. Visual QC can be tedious and time-consuming when the volume of data is important and a branch of work has thus focused on providing automated QC algorithms. In the context of computerized image analysis, such algorithms can be categorized according to the domain on which they operate, namely input (i.e., image) or output (i.e., prediction). Input QC is akin to out-of-distribution detection, aiming at the detection of images that are unusual due for example to the presence of artifacts. Output QC, in contrast, focuses on detecting automated predictions that do not meet expectations. These two facets of QC are intertwined, as noisy images are likely to produce poor predictions. However, they are generally considered as separate problems in the literature and tackled with different methodologies and evaluation procedures. In this chapter, a taxonomy of QC methods is first proposed, oriented to input or output checking. Then, a general framework to jointly combine these two QC facets is proposed and illustrated on two tasks, namely binary segmentation of polyps in endoscopic images and multiclass tumor segmentation in multimodal MRIs.
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