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20 March 2014 A discriminant multi-scale histopathology descriptor using dictionary learning
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When examining a histological sample, an expert must not only identify structures at different scale and conceptual levels, i.e. cellular, tissular and organic, but also recognize and integrate the visual cues of specific pathologies and histological concepts such as “gland", “carcinoma" or “collagen". It is necessary then to code the texture and color so that the relevant information present at different scales is emphasized and preserved. In this article we propose a novel multi-scale image descriptor using dictionaries that learn and code discriminant visual elements associated with specific histological concepts. The dictionaries are built separately for each concept using sparse coding algorithms. The descriptor's discrimination capacity is evaluated using a naive strategy that assigns a particular image to the class best represented by a particular dictionary. Results show how, even using this very simple approach, average recall and precision measures of 0.81 and 0.86 were obtained for the challenging problem of classifying epidermis, eccrine glands, hair follicle and nodular carcinoma in basal skin carcinoma images.
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David Romo, Juan D. García-Arteaga, Pablo Arbeláez, and Eduardo Romero "A discriminant multi-scale histopathology descriptor using dictionary learning", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410Q (20 March 2014);

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