Automated methods for Cobb angle estimation are of high demand for scoliosis
assessment. Existing methods typically calculate the Cobb angle from landmark
estimation, or simply combine the low-level task (e.g., landmark detection and
spine segmentation) with the Cobb angle regression task, without fully
exploring the benefits from each other. In this study, we propose a novel
multi-task framework, named Seg4Reg+, which jointly optimizes the segmentation
and regression networks. We thoroughly investigate both local and global
consistency and knowledge transfer between each other. Specifically, we propose
an attention regularization module leveraging class activation maps (CAMs) from
image-segmentation pairs to discover additional supervision in the regression
network, and the CAMs can serve as a region-of-interest enhancement gate to
facilitate the segmentation task in turn. Meanwhile, we design a novel triangle
consistency learning to train the two networks jointly for global optimization.
The evaluations performed on the public AASCE Challenge dataset demonstrate the
effectiveness of each module and superior performance of our model to the
state-of-the-art methods.

本文提出了一种名为 Seg4Reg+ 的多任务框架，通过优化分割和回归网络，探索了它们之间的局部和全局一致性以及知识迁移，并在公共数据集上进行了评估，证明了其在 Cobb 角估计方面的有效性和卓越表现。

Seg4Reg+: 脊柱分割和 Cobb 角度回归之间的一致性学习

Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle  Regression

We propose regression networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each class. In high dimensional
embedding spaces the direction of data generally contains richer information
than magnitude. Next to this, state-of-the-art few-shot metric methods that
compare distances with aggregated class representations, have shown superior
performance. Combining these two insights, we propose to meta-learn
classification of embedded points by regressing the closest approximation in
every class subspace while using the regression error as a distance metric.
Similarly to recent approaches for few-shot learning, regression networks
reflect a simple inductive bias that is beneficial in this limited-data regime
and they achieve excellent results, especially when more aggregate class
representations can be formed with multiple shots.

本研究提出回归网络，用于解决少样本分类问题。该方法通过在类子空间中回归最近的近似点实现分类，使用回归误差作为距离度量，并将元学习引入到嵌入空间中。使用此简单归纳偏差的回归网络方法在有限数据情况下表现出色，特别是在能够用多个样本形成聚合类表示时。

用于元学习少样本分类的回归网络

Regression Networks for Meta-Learning Few-Shot Classification

Adversarial attacks against neural networks in a regression setting are a
critical yet understudied problem. In this work, we advance the state of the
art by investigating adversarial attacks against regression networks and by
formulating a more effective defense against these attacks. In particular, we
take the perspective that adversarial attacks are likely caused by numerical
instability in learned functions. We introduce a stability inducing,
regularization based defense against adversarial attacks in the regression
setting. Our new and easy to implement defense is shown to outperform prior
approaches and to improve the numerical stability of learned functions.

本文研究了对回归神经网络的对抗攻击问题，并提出了一种基于正则化的稳定性防御方法，该方法表现出了比之前方法更好的效果和改进了函数的数值稳定性。