2020
Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images
Noorbakhsh J, Farahmand S, Foroughi pour A, Namburi S, Caruana D, Rimm D, Soltanieh-ha M, Zarringhalam K, Chuang JH. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nature Communications 2020, 11: 6367. PMID: 33311458, PMCID: PMC7733499, DOI: 10.1038/s41467-020-20030-5.Peer-Reviewed Original ResearchConceptsConvolutional neural networkWhole slide imagesPower of CNNsNormal convolutional neural networkImage data miningColon cancer imagesData miningCNN accuracyCancer imagesNeural networkHistopathological imagesManual inspectionSlide imagesData typesClassifier comparisonSignificant accuracyHistological imagesImage analysisSpatial similarityImagesClassifier pairsClassificationMutation classificationAccuracyMining
2019
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 2019, 16: 703-715. PMID: 31399699, PMCID: PMC6880861, DOI: 10.1038/s41571-019-0252-y.Peer-Reviewed Original ResearchConceptsArtificial intelligenceMachine learning toolsDigital pathologyUse of AIDeep neural networksLearning toolsStained tissue specimensWhole slide imagesFeature-based methodologyNeural networkIntelligencePotential future opportunitiesMorphometric phenotypesNetworkValidation datasetComputational approachToolMiningEnormous divergenceDatasetImagesPrecision oncologyFrameworkComplex processFuture opportunities