Please use this identifier to cite or link to this item: https://dspace.crs4.it/jspui/handle/1138/48
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dc.contributor.authorDel Rio, Mauroen_US
dc.contributor.authorLianas, Lucaen_US
dc.contributor.authorAspegren, Oskaren_US
dc.contributor.authorBusonera, Giovannien_US
dc.contributor.authorVersaci, Francescoen_US
dc.contributor.authorZelic, Renataen_US
dc.contributor.authorVincent, Per Hen_US
dc.contributor.authorLeo, Simoneen_US
dc.contributor.authorPettersson, Andreasen_US
dc.contributor.authorAkre, Olofen_US
dc.contributor.authorPireddu, Lucaen_US
dc.contributor.editorMazzeo, Pier Luigien_US
dc.contributor.editorFrontoni, Emanueleen_US
dc.contributor.editorSclaroff, Stanleyen_US
dc.contributor.editorDistante, Cosimoen_US
dc.date.accessioned2022-09-22T13:55:42Z-
dc.date.available2022-09-22T13:55:42Z-
dc.date.issued2022-08-07-
dc.identifier.urihttps://dspace.crs4.it/jspui/handle/1138/48-
dc.descriptionLink to publisher's page: https://link.springer.com/chapter/10.1007/978-3-031-13321-3_48en_US
dc.description.abstractWhile studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide computationally generated annotations and visual cues to help the pathologist prioritize high-interest image areas. The system includes an image annotation pipeline with DeepHealth-based deep learning models for tissue identification and prostate cancer identification. Annotations are viewed through the platform’s virtual microscope and can be controlled interactively (e.g., thresholding, coloring). Moreover, the platform captures inference provenance information and archives it as RO-Crate artifacts containing data and metadata required for reproducibility. We evaluate the models and the inference pipeline, achieving AUC of 0.986 and 0.969 for tissue and cancer identification, respectively, and verifying linear dependence of execution speed on image tissue content. Finally, we describe the ongoing clinical validation of the contribution, including preliminary results, and discuss feedback from clinical professionals regarding the overall approach.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relationDeep-Learning and HPC to Boost Biomedical Applications for Healthen_US
dc.relationProviding an open collaborative space for digital biology in Europeen_US
dc.relationScalable Visual and Data-intensive Computingen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.subjectDigital pathologyen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectWorkflowsen_US
dc.titleAI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studiesen_US
dc.typeconference paperen_US
dc.relation.conference21st International Conference on IMAGE ANALYSIS AND PROCESSINGen_US
dc.relation.publicationInternational Conference on Image Analysis and Processingen_US
dc.identifier.doi10.1007/978-3-031-13321-3_48-
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationDepartment of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, SE-17176 Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, SE-17176 Stockholm, Sweden KI, Swedenen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationDepartment of Pelvic Cancer, Karolinska University Hospital,SE-17176 Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, SE-17176 Stockholm, Sweden KI, Swedenen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.affiliationDepartment of Pelvic Cancer, Karolinska University Hospital,SE-17176 Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, SE-17176 Stockholm, Sweden KI, Swedenen_US
dc.contributor.affiliationDepartment of Pelvic Cancer, Karolinska University Hospital,SE-17176 Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, SE-17176 Stockholm, Sweden KI, Swedenen_US
dc.contributor.affiliationCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italyen_US
dc.contributor.editoraffiliationConsorzio Nazionale delle Ricerche, Lecce, Italyen_US
dc.contributor.editoraffiliationUniversità Politecnica delle Marche, Ancona, Italyen_US
dc.contributor.editoraffiliationBoston University, Boston, MA, USAen_US
dc.contributor.editoraffiliationConsorzio Nazionale delle Ricerche, Lecce, Italyen_US
dc.relation.isbn978-3-031-13321-3en_US
dc.description.volume13373en_US
dc.relation.grantno825111en_US
dc.relation.grantno824087en_US
dc.relation.grantnoPOR FESR 2014-2020en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairetypeconference paper-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
crisitem.author.deptCenter for Advanced Studies, Research, and Development in Sardinia (CRS4), Pula (CA), Italy-
crisitem.author.orcid0000-0002-8842-5964-
crisitem.author.orcid0000-0002-3750-3029-
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