  Breast cancer is one of the most common cancers affecting women worldwide.
They include a group of malignant neoplasms with a variety of biological,
clinical, and histopathological characteristics. There are more than 35
different histological forms of breast lesions that can be classified and
diagnosed histologically according to cell morphology, growth, and architecture
patterns. Recently, deep learning, in the field of artificial intelligence, has
drawn a lot of attention for the computerized representation of medical images.
Searchable digital atlases can provide pathologists with patch matching tools
allowing them to search among evidently diagnosed and treated archival cases, a
technology that may be regarded as computational second opinion. In this study,
we indexed and analyzed the WHO breast taxonomy (Classification of Tumours 5th
Ed.) spanning 35 tumour types. We visualized all tumour types using deep
features extracted from a state-of-the-art deep learning model, pre-trained on
millions of diagnostic histopathology images from the TCGA repository.
Furthermore, we test the concept of a digital "atlas" as a reference for search
and matching with rare test cases. The patch similarity search within the WHO
breast taxonomy data reached over 88% accuracy when validating through
"majority vote" and more than 91% accuracy when validating using top-n tumour
types. These results show for the first time that complex relationships among
common and rare breast lesions can be investigated using an indexed digital
archive.
