ArticleDiagnostic pathology2011
A supervised visual model for finding regions of interest in basal cell carcinoma images.
Article in Diagnostic pathology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.Journal of biomedical optics · 2024Article
- Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.Journal of digital imaging · 2016Article
- Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image.Computational intelligence and neuroscience · 2016Article
- Quantification of myocardial fibrosis by digital image analysis and interactive stereology.Diagnostic pathology · 2014Article
- Follow-up of breast papillary lesion on core needle biopsy: experience in African-American population.Diagnostic pathology · 2014Article
- CognitionMaster: an object-based image analysis framework.Diagnostic pathology · 2013Article
- Article
- Performance of a simple chromatin-rich segmentation algorithm in quantifying basal cell carcinoma from histology images.BMC research notes · 2012Article
- Pathology imaging informatics for quantitative analysis of whole-slide images.Journal of the American Medical Informatics Association : JAMIAReview
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4 authors.
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Abstract
This paper introduces a supervised learning method for finding diagnostic regions of interest in histopathological images. The method is based on the cognitive process of visual selection of relevant regions that arises during a pathologist's image examination. The proposed strategy emulates the interaction of the visual cortex areas V1, V2 and V4, being the V1 cortex responsible for assigning local levels of relevance to visual inputs while the V2 cortex gathers together these small regions according to some weights modulated by the V4 cortex, which stores some learned rules. This novel strategy can be considered as a complex mix of "bottom-up" and "top-down" mechanisms, integrated by calculating a unique index inside each region. The method was evaluated on a set of 338 images in which an expert pathologist had drawn the Regions of Interest. The proposed method outperforms two state-of-the-art methods devised to determine Regions of Interest (RoIs) in natural images. The quality gain with respect to an adaptated Itti's model which found RoIs was 3.6 dB in average, while with respect to the Achanta's proposal was 4.9 dB.
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