Weakly supervised surgical instrument segmentation with only instrument
presence labels has been rarely explored in surgical domain. To mitigate the
highly under-constrained challenges, we extend a two-stage weakly supervised
segmentation paradigm with temporal attributes from two perspectives. From a
temporal equivariance perspective, we propose a prototype-based temporal
equivariance regulation loss to enhance pixel-wise consistency between adjacent
features. From a semantic continuity perspective, we propose a class-aware
temporal semantic continuity loss to constrain the semantic consistency between
a global view of target frame and local non-discriminative regions of adjacent
reference frame. To the best of our knowledge, WeakSurg is the first
instrument-presence-only weakly supervised segmentation architecture to take
temporal information into account for surgical scenarios. Extensive experiments
are validated on Cholec80, an open benchmark for phase and instrument
recognition. We annotate instance-wise instrument labels with fixed time-steps
which are double checked by a clinician with 3-years experience. Our results
show that WeakSurg compares favorably with state-of-the-art methods not only on
semantic segmentation metrics but also on instance segmentation metrics.

弱监督手术器械分割在手术领域中很少被探索。我们从时间等变性和语义连续性两个角度扩展了一种双阶段弱监督分割范式，提出了基于原型的时间等变性规则损失和类感知的时间语义连续性损失，同时考虑了手术场景中的时间信息。我们基于 Cholec80 数据集进行了广泛实验证明，WeakSurg 不仅在语义分割指标上与最先进的方法相比，而且在实例分割指标上也表现优异。

WeakSurg: 基于时间等变性和语义连续性的弱监督手术器械分割

WeakSurg: Weakly supervised surgical instrument segmentation using  temporal equivariance and semantic continuity

Leveraging network information for prediction tasks has become a common
practice in many domains. Being an important part of targeted marketing,
influencer detection can potentially benefit from incorporating dynamic network
representation. In this work, we investigate different dynamic Graph Neural
Networks (GNNs) configurations for influencer detection and evaluate their
prediction performance using a unique corporate data set. We show that using
deep multi-head attention in GNN and encoding temporal attributes significantly
improves performance. Furthermore, our empirical evaluation illustrates that
capturing neighborhood representation is more beneficial that using network
centrality measures.

本文研究了在影响者检测中，利用动态图神经网络进行预测任务的不同配置，考虑到网络动态性与时间特征属性的影响，证明使用深度多重注意力和编码时间属性可以显著改善性能，在捕获邻域表示方面比使用网络中心度度量更有益。

动态图神经网络进行影响力检测

Influencer Detection with Dynamic Graph Neural Networks

Recent work in synthetic data generation in the time-series domain has
focused on the use of Generative Adversarial Networks. We propose a novel
architecture for synthetically generating time-series data with the use of
Variational Auto-Encoders (VAEs). The proposed architecture has several
distinct properties: interpretability, ability to encode domain knowledge, and
reduced training times. We evaluate data generation quality by similarity and
predictability against four multivariate datasets. We experiment with varying
sizes of training data to measure the impact of data availability on generation
quality for our VAE method as well as several state-of-the-art data generation
methods. Our results on similarity tests show that the VAE approach is able to
accurately represent the temporal attributes of the original data. On next-step
prediction tasks using generated data, the proposed VAE architecture
consistently meets or exceeds performance of state-of-the-art data generation
methods. While noise reduction may cause the generated data to deviate from
original data, we demonstrate the resulting de-noised data can significantly
improve performance for next-step prediction using generated data. Finally, the
proposed architecture can incorporate domain-specific time-patterns such as
polynomial trends and seasonalities to provide interpretable outputs. Such
interpretability can be highly advantageous in applications requiring
transparency of model outputs or where users desire to inject prior knowledge
of time-series patterns into the generative model.

本文提出了一种基于变分自动编码器的新型体系结构，可用于进行时序数据的合成生成，具有可解释性，能够编码领域知识，训练时间较短。实验表明，该方法在数据相似度和预测性方面均表现优异，依然能够准确地呈现原始数据的时态属性，并且对于下一步预测具有显著的改善效果。最后，该模型可以将特定领域的时间模式融入到生成模型中，提供可解释性的输出。