2D/3D image registration to align a 3D volume and 2D X-ray images is a
challenging problem due to its ill-posed nature and various artifacts presented
in 2D X-ray images. In this paper, we propose a multi-agent system with an auto
attention mechanism for robust and efficient 2D/3D image registration.
Specifically, an individual agent is trained with dilated Fully Convolutional
Network (FCN) to perform registration in a Markov Decision Process (MDP) by
observing a local region, and the final action is then taken based on the
proposals from multiple agents and weighted by their corresponding confidence
levels. The contributions of this paper are threefold. First, we formulate
2D/3D registration as a MDP with observations, actions, and rewards properly
defined with respect to X-ray imaging systems. Second, to handle various
artifacts in 2D X-ray images, multiple local agents are employed efficiently
via FCN-based structures, and an auto attention mechanism is proposed to favor
the proposals from regions with more reliable visual cues. Third, a dilated
FCN-based training mechanism is proposed to significantly reduce the Degree of
Freedom in the simulation of registration environment, and drastically improve
training efficiency by an order of magnitude compared to standard CNN-based
training method. We demonstrate that the proposed method achieves high
robustness on both spine cone beam Computed Tomography data with a low
signal-to-noise ratio and data from minimally invasive spine surgery where
severe image artifacts and occlusions are presented due to metal screws and
guide wires, outperforming other state-of-the-art methods (single agent-based
and optimization-based) by a large margin.

该论文提出了一种多智能体系统和自注意力机制相结合的方法，将 2D / 3D 图像配准作为马尔可夫决策过程来解决，通过多个基于全卷积网络的智能体的提案及对应置信度的加权来取得了超过基于单智能体和基于优化方法的最新技术的优异成果。

基于扩张卷积神经网络的多智能体 2D/3D 医学图像配准

Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas.

该篇论文概述了分布式约束优化问题在多智能体系统中的应用，对其各种扩展和解析方法进行了分类并提出了未来研究方向和挑战。

分布式约束优化问题及其应用：综述

Distributed Constraint Optimization Problems and Applications: A Survey

Consider a multi-agent system in a dynamic and uncertain environment. Each
agent's local decision problem is modeled as a Markov decision process (MDP)
and agents must coordinate on a joint action in each period, which provides a
reward to each agent and causes local state transitions. A social planner knows
the model of every agent's MDP and wants to implement the optimal joint policy,
but agents are self-interested and have private local state. We provide an
incentive-compatible mechanism for eliciting state information that achieves
the optimal joint plan in a Markov perfect equilibrium of the induced
stochastic game. In the special case in which local problems are Markov chains
and agents compete to take a single action in each period, we leverage Gittins
allocation indices to provide an efficient factored algorithm and distribute
computation of the optimal policy among the agents. Distributed, optimal
coordinated learning in a multi-agent variant of the multi-armed bandit problem
is obtained as a special case.

对于多智能体系统中的动态和不确定环境，我们提出了一种激励兼容机制，以实现感应随机博弈的马尔可夫完美均衡下的最佳联合计划，特别地，我们运用吉廷斯分配指数提供高效的分布式算法，并将最优策略的计算分配给代理。