SeqSAM通过序列化生成多假设分割掩码，提升医学图像分割的不确定性建模能力。
SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAM
- 采用类似RNN的序列生成方式，逐步输出多个分割掩码。
- 在两个公开数据集上，每个掩码的质量均显著优于单预测方法。
- 支持任意数量的掩码输出，适合临床需要多重假设的场景。
预训练分割模型在医学图像分割中展现出强大而灵活的能力。然而，这些方法通常仅生成单一预测，忽略了医学图像中因边界模糊和标注工具误差带来的固有不确定性。多选择学习可通过多个预测头生成多个掩码，但受限于初始超参数，难以扩展更多输出；且稀疏的胜者通吃损失函数易导致某一个预测头主导，无法保证各掩码的临床相关性。我们提出SeqSAM，一种受RNN启发的序列化多掩码生成方法，采用二分匹配损失确保每个掩码的临床合理性，并可生成任意数量的掩码。我们在两个公开数据集上验证了其有效性，结果显示每个生成掩码的质量均有明显提升。代码已开源：https://github.com/BenjaminTowle/SeqSAM。
原文摘要 · Abstract (English)
Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, this trend has extended to medical imaging. Yet, often these methods only produce a single prediction for a given image, neglecting inherent uncertainty in medical images, due to unclear object boundaries and errors caused by the annotation tool. Multiple Choice Learning is a technique for generating multiple masks, through multiple learned prediction heads. However, this cannot readily be extended to producing more outputs than its initial pre-training hyperparameters, as the sparse, winner-takes-all loss function makes it easy for one prediction head to become overly dominant, thus not guaranteeing the clinical relevancy of each mask produced. We introduce SeqSAM, a sequential, RNN-inspired approach to generating multiple masks, which uses a bipartite matching loss for ensuring the clinical relevancy of each mask, and can produce an arbitrary number of masks. We show notable improvements in quality of each mask produced across two publicly available datasets. Our code is available at https://github.com/BenjaminTowle/SeqSAM.
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