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Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
April 3, 2024, 4:42 a.m. | JungEun Kim, Hangyul Yoon, Geondo Park, Kyungsu Kim, Eunho Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: 4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However, acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration, necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances, not only is data acquisition challenging, but increasing the frame rate for each dataset also proves difficult. To address …
arxiv cs.ai cs.cv cs.lg data eess.iv images intermediate medical type unsupervised
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