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Clinical decision support systems use image processing and machine learning methods to objectively predict cancer in histopathological images. Integral to the development of machine learning classifiers is the ability to generalize from training data to unseen future data. A classification model's ability to accurately predict class label for new unseen data is measured by performance metrics, which...
Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address...
Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or “batch effects” that can commonly occur in histopathological data, especially when images are acquired using different...
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