Deformable image registration (DIR) is crucial in medical image analysis,
enabling the exploration of biological dynamics such as organ motions and
longitudinal changes in imaging. Leveraging Neural Ordinary Differential
Equations (ODE) for registration, this extension work discusses how this
framework can aid in the characterization of sequential biological processes.
Utilizing the Neural ODE's ability to model state derivatives with neural
networks, our Neural Ordinary Differential Equation Optimization-based (NODEO)
framework considers voxels as particles within a dynamic system, defining
deformation fields through the integration of neural differential equations.
This method learns dynamics directly from data, bypassing the need for physical
priors, making it exceptionally suitable for medical scenarios where such
priors are unavailable or inapplicable. Consequently, the framework can discern
underlying dynamics and use sequence data to regularize the transformation
trajectory. We evaluated our framework on two clinical datasets: one for
cardiac motion tracking and another for longitudinal brain MRI analysis.
Demonstrating its efficacy in both 2D and 3D imaging scenarios, our framework
offers flexibility and model agnosticism, capable of managing image sequences
and facilitating label propagation throughout these sequences. This study
provides a comprehensive understanding of how the Neural ODE-based framework
uniquely benefits the image registration challenge.

通过利用神经常微分方程（ODE）进行图像配准，本研究论文讨论了如何利用该框架来帮助表征顺序生物过程，从而更好地理解医学图像分析中的生物动态，该方法可以从数据中直接学习动态，并使用序列数据来规范转换轨迹。

基于神经常微分方程的序贯图像配准用于动态特征描述

Neural Ordinary Differential Equation based Sequential Image  Registration for Dynamic Characterization

Federated learning facilitates the collaborative learning of a global model
across multiple distributed medical institutions without centralizing data.
Nevertheless, the expensive cost of annotation on local clients remains an
obstacle to effectively utilizing local data. To mitigate this issue, federated
active learning methods suggest leveraging local and global model predictions
to select a relatively small amount of informative local data for annotation.
However, existing methods mainly focus on all local data sampled from the same
domain, making them unreliable in realistic medical scenarios with domain
shifts among different clients. In this paper, we make the first attempt to
assess the informativeness of local data derived from diverse domains and
propose a novel methodology termed Federated Evidential Active Learning (FEAL)
to calibrate the data evaluation under domain shift. Specifically, we introduce
a Dirichlet prior distribution in both local and global models to treat the
prediction as a distribution over the probability simplex and capture both
aleatoric and epistemic uncertainties by using the Dirichlet-based evidential
model. Then we employ the epistemic uncertainty to calibrate the aleatoric
uncertainty. Afterward, we design a diversity relaxation strategy to reduce
data redundancy and maintain data diversity. Extensive experiments and analyses
are conducted to show the superiority of FEAL over the state-of-the-art active
learning methods and the efficiency of FEAL under the federated active learning
framework.

通过在医疗领域分布式的多个机构之间协作学习一个全局模型，联合学习有助于实现数据无需集中的目标。本研究首次尝试评估来自不同领域的本地数据的信息量，并提出了一种称为 Federated Evidential Active Learning (FEAL) 的新方法来处理领域转移下的数据评估。通过引入 Dirichlet 先验分布，我们能够将预测视为概率空间上的分布，并利用基于 Dirichlet 的证据模型来捕获不确定性。然后，我们使用这种不确定性来校准随机不确定性，并采用多样性放松策略来减少数据冗余并保持数据多样性。通过广泛的实验和分析，我们证明了 FEAL 在联合主动学习框架下优于现有主动学习方法的特点和效率。