Mask-TS Net：用于息肉分割的掩码温度缩放不确定性校准
Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation
AI总结：
针对息肉分割中病变区域占比小、模型对病灶校准不足的问题，提出结合 Mask-Loss 与 Mask-TS 的四分支校准网络，聚焦潜在病灶 logits 缩放，实验表明其优于现有校准方法。
摘要：
医学图像中许多流行的校准方法都集中于分类，但关于语义分割的可比研究却很少。在医学图像的息肉分割中，我们发现大多数病变区域仅占整幅图像的一小部分，这导致以往模型尽管在总体上看似具有更好的期望校准误差（ECE）分数，但对病变区域的校准并不好，而对背景的校准较好。因此，我们提出了带有 Mask-Loss 和 Mask-TS 策略的四分支校准网络，以更加关注潜在病变区域内 logits 的缩放，从而缓解背景干扰的影响。在实验中，我们将现有校准方法与所提出的掩码温度缩放（Mask-TS）进行比较。结果表明，所提出的校准网络在定性和定量方面均优于其他方法。
英文摘要：
Lots of popular calibration methods in medical images focus on classification, but there are few comparable studies on semantic segmentation. In polyp segmentation of medical images, we find most diseased area occupies only a small portion of the entire image, resulting in previous models being not well-calibrated for lesion regions but well-calibrated for background, despite their seemingly better Expected Calibration Error (ECE) scores overall. Therefore, we proposed four-branches calibration network with Mask-Loss and Mask-TS strategies to more focus on the scaling of logits within potential lesion regions, which serves to mitigate the influence of background interference. In the experiments, we compare the existing calibration methods with the proposed Mask Temperature Scaling (Mask-TS). The results indicate that the proposed calibration network outperforms other methods both qualitatively and quantitatively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
