2021
Artificial intelligence applied to breast pathology
Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Archiv 2021, 480: 191-209. PMID: 34791536, DOI: 10.1007/s00428-021-03213-3.Peer-Reviewed Original ResearchConceptsArtificial intelligenceApplication of AIComplex artificial intelligenceDevelopment of algorithmsComputer visionDeep learningMachine learningMitosis detectionDigital pathologyNeural networkDigital dataHistology imagesTissue segmentationField of pathologyImage analysisIntelligencePromising resultsTaskLearningImagesSegmentationBreast pathologyComputerAlgorithmNetwork
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 opportunitiesPrediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
Robinson E, Kulkarni P, Pradhan J, Gartrell R, Yang C, Rizk E, Acs B, Rohr B, Phelps R, Ferringer T, Horst B, Rimm D, Wang J, Saenger Y. Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning. Journal Of Clinical Oncology 2019, 37: 9577-9577. DOI: 10.1200/jco.2019.37.15_suppl.9577.Peer-Reviewed Original ResearchDeep neural net architectureOpen source softwareRecurrent neural networkNeural net architectureDigital pathology toolsDeep learningSource softwareNet architectureFeature informationNeural networkNetwork parametersTIFF filesAdjuvant immunotherapyMelanoma recurrenceCohort 2Cohort 1Cell classificationStage IMultivariable Cox proportional hazards modelsDNNCox proportional hazards modelColumbia University Medical CenterNuclear segmentationEvidence of diseaseIndependent prognostic factor
2018
Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens
Rawat RR, Ruderman D, Macklin P, Rimm DL, Agus DB. Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens. Npj Breast Cancer 2018, 4: 32. PMID: 30211313, PMCID: PMC6123433, DOI: 10.1038/s41523-018-0084-4.Peer-Reviewed Original ResearchEstrogen receptor statusInvasive ductal carcinomaER statusEstrogen receptorReceptor statusPercent of cellsHormonal therapyDuctal carcinomaImmunohistochemistry stainingHistology patternPathway statusPatient samplesPathway activationPilot studyNuclear featuresDeep neural networksTissue coresNeural networkReceptorsFuture studiesStatusTissue morphologyBiological featuresDeep learning approachCarcinoma