Diagnostic pathology is the foundation and gold standard for identifying carcinomas. However, high inter-observer variability substantially affects productivity in routine pathology and is especially ubiquitous in diagnostician-deficient medical centres. Despite rapid growth in computer-aided diagnosis (CAD), the application of whole-slide pathology diagnosis remains impractical. This paper presents a novel pathology whole-slide diagnosis method, powered by artificial intelligence, to address the lack of interpretable diagnosis.
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