Abstract
Deep learning based computer vision systems are used in many fields for a variety of classification and identification tasks. They are composed of a network that learns features, rather than traditional hand-curated rules. The diagnosis of human pathology is primarily based on the visual features in histology slides. These slides are prepared by sampling a small amount of the original tissue and interpretation is sometimes limited by paucity of visual material. To what extent can computer systems, which create their own visual feature map, be used to make diagnoses where visual information is limited? We present our findings in the context of renal biopsies.
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