Featured Publications
Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
Joel M, Avesta A, Yang D, Zhou J, Omuro A, Herbst R, Krumholz H, Aneja S. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers 2023, 15: 1548. PMID: 36900339, PMCID: PMC10000732, DOI: 10.3390/cancers15051548.Peer-Reviewed Original ResearchAdversarial imagesDeep learning modelsDL modelsDetection modelLearning modelConvolutional neural networkDetection schemeAdversarial detectionDefense techniquesMachine learningMedical imagesAdversarial perturbationsInput imageAdversarial trainingNeural networkArt performanceMagnetic resonance imagingGradient descentPixel valuesHigh accuracyImagesBrain magnetic resonance imagingAbsence of malignancyClassificationScheme
2024
Enhancing Clinical Decision-Making: An Externally Validated Machine Learning Model for Predicting IDH Mutation in Gliomas using Radiomics from Pre-Surgical MRI
Lost J, Ashraf N, Jekel L, von Reppert M, Tillmanns N, Willms K, Merkaj S, Petersen G, Avesta A, Ramakrishnan D, Omuro A, Nabavizadeh A, Bakas S, Bousabarah K, De Lin M, Aneja S, Sabel M, Aboian M. Enhancing Clinical Decision-Making: An Externally Validated Machine Learning Model for Predicting IDH Mutation in Gliomas using Radiomics from Pre-Surgical MRI. Neuro-Oncology Advances 2024, vdae157. DOI: 10.1093/noajnl/vdae157.Peer-Reviewed Original ResearchIsocitrate dehydrogenase mutation statusArea under the curveMagnetic resonance imagingMutation statusML modelsMachine learningSemi-automated tumour segmentationsPre-surgical magnetic resonance imagingCare of glioma patientsMachine learning modelsClinical care of glioma patientsIsocitrate dehydrogenase statusAnnotated datasetsFeature extractionPrediction taskMulti-institutional dataModel trainingIDH mutationsPredicting IDH mutationLearning modelsRetrospective studyHeterogeneous datasetsTumor segmentationGlioma patientsBrain tumors
2023
The Yale Glioma Dataset: Developing An Open Access, Annotated MRI Database
Sala M, Lost J, Tillmanns N, Merkaj S, von Reppert M, Ramakrishnan D, Bousabarah K, Huttner A, Aneja S, Avesta A, Omuro A, Aboian M. The Yale Glioma Dataset: Developing An Open Access, Annotated MRI Database. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/4511.Peer-Reviewed Original ResearchBladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence
Moore N, McWilliam A, Aneja S. Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Seminars In Radiation Oncology 2023, 33: 70-75. PMID: 36517196, DOI: 10.1016/j.semradonc.2022.10.009.Peer-Reviewed Original ResearchConceptsArtificial intelligenceMachine learningReliability of algorithmAccurate predictive modelsEfficient creationIntelligenceBladder cancer patientsRadiation oncology patientsAlgorithmPrognostic modellingRoutine clinical useClinical outcomesOncology patientsClinical recordsCancer patientsBladder cancerPredictive modelTreatment planClinical useMultiple treatment plansClinical implementationNext stepRadiation oncologyTreatment planningInterpretability