GDM reformulates 3D conditional medical image generation as attractive-repulsive drifting with multi-level feature banks to balance distribution plausibility, patient fidelity, and one-step inference, outperforming GANs, flows, and SDEs on MRI-to-CT and sparse CT tasks.
Totalsegmen- tator: robust segmentation of 104 anatomic structures in CT images,
2 Pith papers cite this work. Polarity classification is still indexing.
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PENGWIN challenge results show top CT segmentation IoU of 0.93 but X-ray IoU of 0.77, indicating progress in CT but challenges in X-ray due to overlaps and fragment ambiguities.
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Generative Drifting for Conditional Medical Image Generation
GDM reformulates 3D conditional medical image generation as attractive-repulsive drifting with multi-level feature banks to balance distribution plausibility, patient fidelity, and one-step inference, outperforming GANs, flows, and SDEs on MRI-to-CT and sparse CT tasks.
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Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
PENGWIN challenge results show top CT segmentation IoU of 0.93 but X-ray IoU of 0.77, indicating progress in CT but challenges in X-ray due to overlaps and fragment ambiguities.