The surgical intervention is crucial to patient healthcare, and many studies
have developed advanced algorithms to provide understanding and decision-making
assistance for surgeons. Despite great progress, these algorithms are developed
for a single specific task and scenario, and in practice require the manual
combination of different functions, thus limiting the applicability. Thus, an
intelligent and versatile surgical assistant is expected to accurately
understand the surgeon's intentions and accordingly conduct the specific tasks
to support the surgical process. In this work, by leveraging advanced
multimodal large language models (MLLMs), we propose a Versatile Surgery
Assistant (VS-Assistant) that can accurately understand the surgeon's intention
and complete a series of surgical understanding tasks, e.g., surgical scene
analysis, surgical instrument detection, and segmentation on demand.
Specifically, to achieve superior surgical multimodal understanding, we devise
a mixture of projectors (MOP) module to align the surgical MLLM in VS-Assistant
to balance the natural and surgical knowledge. Moreover, we devise a surgical
Function-Calling Tuning strategy to enable the VS-Assistant to understand
surgical intentions, and thus make a series of surgical function calls on
demand to meet the needs of the surgeons. Extensive experiments on neurosurgery
data confirm that our VS-Assistant can understand the surgeon's intention more
accurately than the existing MLLM, resulting in overwhelming performance in
textual analysis and visual tasks. Source code and models will be made public.

通过利用高级多模态大型语言模型，我们提出了一种具有智能和多功能的手术助理（VS-Assistant），可以准确理解外科医生的意图，并完成一系列手术理解任务，例如手术场景分析、手术器械检测和需求分割。

VS 助手：满足外科医生需求的多功能手术助手

VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons

Epilepsy is one of the most common neurological disorders, and many patients
require surgical intervention when medication fails to control seizures. For
effective surgical outcomes, precise localisation of the epileptogenic focus -
often approximated through the Seizure Onset Zone (SOZ) - is critical yet
remains a challenge. Active probing through electrical stimulation is already
standard clinical practice for identifying epileptogenic areas. This paper
advances the application of deep learning for SOZ localisation using Single
Pulse Electrical Stimulation (SPES) responses. We achieve this by introducing
Transformer models that incorporate cross-channel attention. We evaluate these
models on held-out patient test sets to assess their generalisability to unseen
patients and electrode placements.
Our study makes three key contributions: Firstly, we implement an existing
deep learning model to compare two SPES analysis paradigms - namely, divergent
and convergent. These paradigms evaluate outward and inward effective
connections, respectively. Our findings reveal a notable improvement in moving
from a divergent (AUROC: 0.574) to a convergent approach (AUROC: 0.666),
marking the first application of the latter in this context. Secondly, we
demonstrate the efficacy of the Transformer models in handling heterogeneous
electrode placements, increasing the AUROC to 0.730. Lastly, by incorporating
inter-trial variability, we further refine the Transformer models, with an
AUROC of 0.745, yielding more consistent predictions across patients. These
advancements provide a deeper insight into SOZ localisation and represent a
significant step in modelling patient-specific intracranial EEG electrode
placements in SPES. Future work will explore integrating these models into
clinical decision-making processes to bridge the gap between deep learning
research and practical healthcare applications.

这篇论文介绍了使用单脉冲电刺激（SPES）响应来进行癫痫发生区（SOZ）定位的深度学习应用，通过引入具有跨通道注意力的 Transformer 模型，在持有的患者测试数据集上评估其对未知患者和电极放置位置的泛化能力，表明转从发散方法（AUROC：0.574）到收敛方法（AUROC：0.666）能够显著改善定位效果，并展示了 Transformer 模型在处理异构电极放置方面的有效性，使 AUROC 提高至 0.730，最后通过考虑试验间变异性进一步优化 Transformer 模型，达到 AUROC 0.745，增强了患者之间的一致性预测，这些进展为 SOZ 定位提供了更深入的了解，并在 SPES 中对患者特异性的颅内脑电电极放置建模方面迈出了重要的一步。未来的工作将探索将这些模型整合到临床决策过程中，将深度学习研究与实际的医疗应用桥接起来。

使用 Transformer 从单脉冲电刺激响应中定位癫痫发作区

Localising the Seizure Onset Zone from Single-Pulse Electrical  Stimulation Responses with a Transformer

There exist well-developed frameworks for causal modelling, but these require
rather a lot of human domain expertise to define causal variables and perform
interventions. In order to enable autonomous agents to learn abstract causal
models through interactive experience, the existing theoretical foundations
need to be extended and clarified. Existing frameworks give no guidance
regarding variable choice / representation, and more importantly, give no
indication as to which behaviour policies or physical transformations of state
space shall count as interventions. The framework sketched in this paper
describes actions as transformations of state space, for instance induced by an
agent running a policy. This makes it possible to describe in a uniform way
both transformations of the micro-state space and abstract models thereof, and
say when the latter is veridical / grounded / natural. We then introduce
(causal) variables, define a mechanism as an invariant predictor, and say when
an action can be viewed as a ``surgical intervention'', thus bringing the
objective of causal representation \& intervention skill learning into clearer
focus.

本文论述一种框架，通过交互式体验使自主代理学习抽象因果模型需要扩展并澄清现有的理论基础，该框架将动作描述为状态空间的转换，从而使描述微状态空间的转换和抽象模型的转换成为可能，进而将因果表示和干预技能学习的目标变得更加清晰。