March 14, 2024, 4:47 a.m. | Chuang Niu, Qing Lyu, Christopher D. Carothers, Parisa Kaviani, Josh Tan, Pingkun Yan, Mannudeep K. Kalra, Christopher T. Whitlow, Ge Wang

cs.CV updates on

arXiv:2304.02649v2 Announce Type: replace-cross
Abstract: Patient management requires multitasking interaction with multimodal data. While today's AI, particularly large foundation models, promises unprecedented opportunities, progress remains relatively slow in developing medical multimodal multitask foundation models. There are two main challenges along this direction: the data challenge -- the high bar to curate medical multimodal multitask datasets including 3D medical tomographic images in alignment with other clinical datasets, and the model challenge -- the unavailability of a scalable and adaptable foundation model …

abstract arxiv challenge challenges data eess.iv foundation foundation model management medical multimodal multimodal data multitasking opportunities patient performance progress type

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