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- W4313305462 abstract "The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the field of medicine, having a diverse set of training data on a particular disease can lead to the development of a model that is able to accurately predict the disease. However, despite the potential benefits, there have not been significant advances in image-based diagnosis due to a lack of high-quality annotated data. This article highlights the importance of using a data-centric approach to improve the quality of data representations, particularly in cases where the available data is limited. To address this small-data issue, we discuss four methods for generating and aggregating training data: data augmentation, transfer learning, federated learning, and GANs (generative adversarial networks). We also propose the use of knowledge-guided GANs to incorporate domain knowledge in the training data generation process. With the recent progress in large pre-trained language models, we believe it is possible to acquire high-quality knowledge that can be used to improve the effectiveness of knowledge-guided generative methods." @default.
- W4313305462 created "2023-01-06" @default.
- W4313305462 creator A5013545831 @default.
- W4313305462 date "2022-12-27" @default.
- W4313305462 modified "2023-10-16" @default.
- W4313305462 title "Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions" @default.
- W4313305462 doi "https://doi.org/10.48550/arxiv.2212.13591" @default.
- W4313305462 hasPublicationYear "2022" @default.
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