SynerMedGen introduces generation-aligned understanding tasks and a two-stage training strategy that enables strong zero-shot medical image synthesis performance and outperforms specialized models when generation training is added.
Medical Image Analysis , volume=
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.
citing papers explorer
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SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment
SynerMedGen introduces generation-aligned understanding tasks and a two-stage training strategy that enables strong zero-shot medical image synthesis performance and outperforms specialized models when generation training is added.
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ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.