  Patching gigapixel whole slide images (WSIs) is an important task in
computational pathology. Some methods have been proposed to select a subset of
patches as WSI representation for downstream tasks. While most of the
computational pathology tasks are designed to classify or detect the presence
of pathological lesions in each WSI, the confounding role and redundant nature
of normal histology in tissue samples are generally overlooked in WSI
representations. In this paper, we propose and validate the concept of an
"atlas of normal tissue" solely using samples of WSIs obtained from normal
tissue biopsies. Such atlases can be employed to eliminate normal fragments of
tissue samples and hence increase the representativeness collection of patches.
We tested our proposed method by establishing a normal atlas using 107 normal
skin WSIs and demonstrated how established indexes and search engines like
Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma
(cSCC) to show the advantage. We also validated our method applied to an
external dataset of 451 breast WSIs. The number of selected WSI patches was
reduced by 30% to 50% after utilizing the proposed normal atlas while
maintaining the same indexing and search performance in leave-one-patinet-out
validation for both datasets. We show that the proposed normal atlas shows
promise for unsupervised selection of the most representative patches of the
abnormal/malignant WSI lesions.
