Accurate representation of tooth position is extremely important in
treatment. 3D dental image segmentation is a widely used method, however
labelled 3D dental datasets are a scarce resource, leading to the problem of
small samples that this task faces in many cases. To this end, we address this
problem with a pretrained SAM and propose a novel 3D-U-SAM network for 3D
dental image segmentation. Specifically, in order to solve the problem of using
2D pre-trained weights on 3D datasets, we adopted a convolution approximation
method; in order to retain more details, we designed skip connections to fuse
features at all levels with reference to U-Net. The effectiveness of the
proposed method is demonstrated in ablation experiments, comparison
experiments, and sample size experiments.

准确的牙位表示在治疗中非常重要。用于三维牙齿图像分割的预训练 SAM 和我们提出的三维牙齿图像分割网络 3D-U-SAM 解决了样本稀缺的问题，并通过削减逼近方法和 U-Net 参考的跳跃连接来提高细节保留能力。通过消融实验、对比实验和样本大小实验证明了该方法的有效性。

3D-U-SAM 网络用于 CBCT 图像中的少样本牙齿分割

3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images

A commonly observed pattern in machine learning models is an underprediction
of the target feature, with the model's predicted target rate for members of a
given category typically being lower than the actual target rate for members of
that category in the training set. This underprediction is usually larger for
members of minority groups; while income level is underpredicted for both men
and women in the 'adult' dataset, for example, the degree of underprediction is
significantly higher for women (a minority in that dataset). We propose that
this pattern of underprediction for minorities arises as a predictable
consequence of statistical inference on small samples. When presented with a
new individual for classification, an ML model performs inference not on the
entire training set, but on a subset that is in some way similar to the new
individual, with sizes of these subsets typically following a power law
distribution so that most are small (and with these subsets being necessarily
smaller for the minority group). We show that such inference on small samples
is subject to systematic and directional statistical bias, and that this bias
produces the observed patterns of underprediction seen in ML models. Analysing
a standard sklearn decision tree model's predictions on a set of over 70
subsets of the 'adult' and COMPAS datasets, we found that a bias prediction
measure based on small-sample inference had a significant positive correlations
(0.56 and 0.85) with the observed underprediction rate for these subsets.

在机器学习模型中，对小众群体的收入水平等指标的预测偏低通常源于样本量过小导致的系统性和方向性统计偏差。