Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2019  |  Volume : 10  |  Issue : 1  |  Page : 5

Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks


1 Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo College of Medicine, Rochester, Minnesota, USA
2 Department of Laboratory Medicine and Pathology, Mayo College of Medicine, Rochester, Minnesota, USA

Correspondence Address:
Dr. Steven N Hart
Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo College of Medicine, Rochester, Minnesota
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_32_18

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Whole-slide images (WSIs) are a rich new source of biomedical imaging data. The use of automated systems to classify and segment WSIs has recently come to forefront of the pathology research community. While digital slides have obvious educational and clinical uses, their most exciting potential lies in the application of quantitative computational tools to automate search tasks, assist in classic diagnostic classification tasks, and improve prognosis and theranostics. An essential step in enabling these advancements is to apply advances in machine learning and artificial intelligence from other fields to previously inaccessible pathology datasets, thereby enabling the application of new technologies to solve persistent diagnostic challenges in pathology. Here, we applied convolutional neural networks to differentiate between two forms of melanocytic lesions (Spitz and conventional). Classification accuracy at the patch level was 99.0%–2% when applied to WSI. Importantly, when the model was trained without careful image curation by a pathologist, the training took significantly longer and had lower overall performance. These results highlight the utility of augmented human intelligence in digital pathology applications, and the critical role pathologists will play in the evolution of computational pathology algorithms.


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