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Object knowledge-aware multiple instance learning for small tumor segmentation
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DOI：10.1016/j.bspc.2025.109400.png)
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Abstract
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• Using bounding box annotations to train segmentation model can save time and cost. • Loose bounding boxes lack the tightness prior required for traditional MIL methods. • The accurate of OKMIL is approach to that of fully supervised segmentation methods. • The accurate of tiny tumor was improved by 14.85% compared to SOTA methods.
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