Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Deep learning compression cuts whole slide image sizes by 43-72% versus JPEG, with glass removal adding 0.3-33% savings and combined methods reaching 44-80%, while keeping SSIM above 0.95 on tissue patches.
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Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching
Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
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Deep learning-based compression of giga-resolution whole slide images
Deep learning compression cuts whole slide image sizes by 43-72% versus JPEG, with glass removal adding 0.3-33% savings and combined methods reaching 44-80%, while keeping SSIM above 0.95 on tissue patches.