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How to make Biomedical Imaging Datasets AI-ready?

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Názov: How to make Biomedical Imaging Datasets AI-ready?
Autori: Dvoretskii, Stefan, Josh, Moore, Schader, Philipp, Kulla, Lucas, Nolden, Marco
Informácie o vydavateľovi: Zenodo, 2024.
Rok vydania: 2024
Predmety: Artificial intelligence, Work Performance/statistics & numerical data, Abstracting and Indexing/statistics & numerical data, Data exchange, Bioimaging, Academies and Institutes/statistics & numerical data, Cognitive Neuroscience/statistics & numerical data
Popis: The vast amount of observations needed to train new generation AI models (Foundation Models) necessitates a strategy of combining data from multiple repositories in a semi-automatic way to minimize human involvement. However, many public data sources present challenges such as inhomogeneity, lack of machine-actionable data, and manual access barriers. These issues can be mitigated through the consequent adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles, as well as state-of-the-art data standards and tools. In the poster, we highlight the inhomogeneity of the schema definitions in the field, provide helpful tips on what could improve the AI-readiness of data and inspect example data sources which implement the most novel concepts in working with data and metadata in the machine-actionable fashion.
Druh dokumentu: Conference object
DOI: 10.5281/zenodo.14041301
DOI: 10.5281/zenodo.14041300
DOI: 10.5281/zenodo.14217316
Rights: CC BY
Prístupové číslo: edsair.doi.dedup.....35ad3dc9d1a52a8a5bfe1de9cc530f2f
Databáza: OpenAIRE
Popis
Abstrakt:The vast amount of observations needed to train new generation AI models (Foundation Models) necessitates a strategy of combining data from multiple repositories in a semi-automatic way to minimize human involvement. However, many public data sources present challenges such as inhomogeneity, lack of machine-actionable data, and manual access barriers. These issues can be mitigated through the consequent adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles, as well as state-of-the-art data standards and tools. In the poster, we highlight the inhomogeneity of the schema definitions in the field, provide helpful tips on what could improve the AI-readiness of data and inspect example data sources which implement the most novel concepts in working with data and metadata in the machine-actionable fashion.
DOI:10.5281/zenodo.14041301