04885nam 2201189z- 450 991055735350332120220111(CKB)5400000000042355(oapen)https://directory.doabooks.org/handle/20.500.12854/77043(oapen)doab77043(EXLCZ)99540000000004235520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Computational Methods for Oncological Image AnalysisBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (262 p.)3-0365-2554-8 3-0365-2555-6 [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.]Medicine and Nursingbicssc3D-CNNbone scintigraphybrain MRI imagebrain tumorbrain tumor segmentationBRATS datasetbreast cancerbreast cancer detectionbreast cancer diagnosisbreast imagingbreast massclassificationclutter rejectioncomputer-aided detectioncontrast source inversiondataset partitiondeep learningdimensionality reductionensemble classificationensemble methodfalse positives reductionfeature selectionimage reconstructionimaging biomarkersimmunotherapyincoherent imaginginterferometric optical fibersk-means clusteringKolmogorov-Smirnov hypothesis testmachine learningmagnetic resonance imagingmammographyMask R-CNNmass detectionmass segmentationmedical imagingmelanoma detectionmicrowave imagingMRgFUSn/aperformance metricsprincipal component analysisprostate cancerproton resonance frequency shiftradiomicsRBF neural networksreferenceless thermometryregion growingrisk assessmentsegmentationself-attentionsemisupervised classificationshallow machine learningskull strippingstatistical inferencesurveytemperature variationstexturetransfer learningtumor regionU-Netunsupervised machine learningWisconsin Breast Cancer DatasetMedicine and NursingRundo Leonardoedt1290017Militello CarmeloedtConti VincenzoedtZaccagna FulvioedtHan ChangheeedtRundo LeonardoothMilitello CarmeloothConti VincenzoothZaccagna FulvioothHan ChangheeothBOOK9910557353503321Advanced Computational Methods for Oncological Image Analysis3021302UNINA