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001032397 005__ 20241212210724.0
001032397 037__ $$aFZJ-2024-06207
001032397 041__ $$aEnglish
001032397 1001_ $$0P:(DE-Juel1)191494$$aPfaehler, Elisabeth$$b0$$eCorresponding author$$ufzj
001032397 1112_ $$aInformatica Feminale$$d2024-08-19 - 2024-08-21$$wGermany
001032397 245__ $$aExplainable AI in Medical Image Analysis
001032397 260__ $$c2024
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001032397 3367_ $$2BibTeX$$aMISC
001032397 3367_ $$0PUB:(DE-HGF)17$$2PUB:(DE-HGF)$$aLecture$$blecture$$mlecture$$s1734014473_3217$$xOther
001032397 3367_ $$2ORCID$$aLECTURE_SPEECH
001032397 3367_ $$2DataCite$$aText
001032397 500__ $$aElisabeth Pfaehler is funded by the European Union, Marie-Curie Sklodowska Fellowship HORIZON-MSCA-2021-PF-01, grant 101068572.
001032397 520__ $$aThe use of Artificial Intelligence (AI) in the medical domain is of high interest. AI could facilitate the work of physicians and guide them in their clinical decision-making. However, many AI-based methods are still a black box and hardly understood. As every patient has the right to an explainable diagnosis, it is important to understand and explain the processes and reasons behind the decisions of Convolutional Neural Networks (CNNs). In this workshop, we will explain the different applications of AI and Explainable AI in medical image analysis. The participants will learn about the different Explainable AI methods, their limitations, and how they could be included in a clinical workflow. Some Explainable AI methods will be applied to examples.
001032397 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001032397 7001_ $$0P:(DE-Juel1)196726$$aKrieger, Lena$$b1$$eCorresponding author$$ufzj
001032397 909CO $$ooai:juser.fz-juelich.de:1032397$$pVDB
001032397 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191494$$aForschungszentrum Jülich$$b0$$kFZJ
001032397 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)196726$$aForschungszentrum Jülich$$b1$$kFZJ
001032397 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001032397 9141_ $$y2024
001032397 920__ $$lyes
001032397 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
001032397 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x1
001032397 980__ $$alecture
001032397 980__ $$aVDB
001032397 980__ $$aI:(DE-Juel1)IAS-8-20210421
001032397 980__ $$aI:(DE-Juel1)INM-4-20090406
001032397 980__ $$aUNRESTRICTED