SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
نویسندگان
چکیده
Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term aging) factors. Evidently, prior knowledge these factors will be beneficial when modeling their future state, i.e., via image generation. However, most the medical generation tasks only rely on input from single image, thus ignoring sequential dependency even longitudinal data is available. Sequence-aware deep generative models, where model sequence ordered timestamped images, are still underexplored in imaging domain that featured by several unique challenges: 1) Sequences with various lengths; 2) Missing or frame, 3) High dimensionality. To this end, we propose sequence-aware diffusion (SADM) for images. Recently, models have shown promising results high-fidelity Our method extends new technique introducing transformer as conditional module model. The novel design enables learning missing during training allows autoregressive images inference. extensive experiments 3D demonstrate effectiveness SADM compared baselines alternative methods. code available at https://github.com/ubc-tea/SADM-Longitudinal-Medical-Image-Generation.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-34048-2_30