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| Hauptseite > Publikationsdatenbank > Explainable AI in Medical Image Analysis > print |
| 001 | 1032397 | ||
| 005 | 20241212210724.0 | ||
| 037 | _ | _ | |a FZJ-2024-06207 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Pfaehler, Elisabeth |0 P:(DE-Juel1)191494 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Informatica Feminale |d 2024-08-19 - 2024-08-21 |w Germany |
| 245 | _ | _ | |a Explainable AI in Medical Image Analysis |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a lecture |2 DRIVER |
| 336 | 7 | _ | |a Generic |0 31 |2 EndNote |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Lecture |b lecture |m lecture |0 PUB:(DE-HGF)17 |s 1734014473_3217 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Text |2 DataCite |
| 500 | _ | _ | |a Elisabeth Pfaehler is funded by the European Union, Marie-Curie Sklodowska Fellowship HORIZON-MSCA-2021-PF-01, grant 101068572. |
| 520 | _ | _ | |a The 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 700 | 1 | _ | |a Krieger, Lena |0 P:(DE-Juel1)196726 |b 1 |e Corresponding author |u fzj |
| 909 | C | O | |o oai:juser.fz-juelich.de:1032397 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)191494 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)196726 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-4-20090406 |k INM-4 |l Physik der Medizinischen Bildgebung |x 1 |
| 980 | _ | _ | |a lecture |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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