June 5, 2024, 4:44 a.m. | Colin Hansen, Simas Glinskis, Ashwin Raju, Micha Kornreich, JinHyeong Park, Jayashri Pawar, Richard Herzog, Li Zhang, Benjamin Odry

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02477v1 Announce Type: cross
Abstract: Data driven models for automated diagnosis in radiology suffer from insufficient and imbalanced datasets due to low representation of pathology in a population and the cost of expert annotations. Datasets can be bolstered through data augmentation. However, even when utilizing a full suite of transformations during model training, typical data augmentations do not address variations in human anatomy. An alternative direction is to synthesize data using generative models, which can potentially craft datasets with specific …

abstract annotations arxiv augmentation automated cost cs.cv cs.lg data datasets diagnosis diffusion eess.iv expert however inpainting low mri pathology population radiology representation through type

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