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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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摘要
En 中文
• 使用边界框标注来训练分割模型可以节省时间和成本。
• 松散的边界框缺乏传统MIL方法所需的紧致性先验。
• OKMIL的准确率接近全监督分割方法的准确率。
• 相比SOTA方法，微小肿瘤的准确率提高了14.85%。
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
机构
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