Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.

图形神经网络（GNNS）在图表表示学习中获得了动力，并在各种领域（例如数据挖掘）（\ emph {e.g。，}社交网络分析和推荐系统），计算机视觉（\ emph {例如，}对象检测和点云学习）和自然语言处理（\ emph {e.g。，}关系提取和序列学习），仅举几例。随着自然语言处理和计算机视觉中变压器的出现，图形变压器将图形结构嵌入到变压器体系结构中，以克服局部邻域聚集的局限性，同时避免严格的结构电感偏见。在本文中，我们从面向任务的角度介绍了计算机视觉中GNN和图形变压器的全面综述。具体来说，我们根据输入数据的模式，\ emph {i.e。，} 2D自然图像，视频，3D数据，Vision +语言和医学图像，将其在计算机视觉中的应用分为五个类别。在每个类别中，我们根据一组视觉任务进一步对应用程序进行划分。这种面向任务的分类法使我们能够检查如何通过不同的基于GNN的方法以及这些方法的表现如何解决每个任务。基于必要的初步，我们提供了任务的定义和挑战，对代表性方法的深入报道以及有关见解，局限性和未来方向的讨论。

医学图像分类已在医学图像分析中广泛采用。但是，由于难以在医疗领域收集和标记数据，医疗图像数据集通常受到高度影响。为了解决这个问题，先前的工作利用类样本作为重新加权或重新采样的先验，但特征表示通常仍然不够歧视。在本文中，我们采用对比度学习来解决长尾医疗失衡问题。具体而言，我们首先提出类别原型和对抗性原型，以产生代表性的对比对。然后，提出了原型重新校准策略来解决高度不平衡的数据分布。最后，统一的原始损失旨在训练我们的框架。总体框架，即作为原型的对比学习（PROCO），以端到端方式统一为单级管道，以减轻医学图像分类中的不平衡问题，这也是与现有作品的独特进步当他们遵循传统的两阶段管道时。对两个高度平衡的医学图像分类数据集进行了广泛的实验表明，我们的方法的表现优于现有的最新方法。

数据采集​​和注释中的困难基本上限制了3D医学成像应用的训练数据集的样本尺寸。结果，在没有足够的预训练参数的情况下，构建来自划痕的高性能3D卷积神经网络仍然是一项艰巨的任务。以前关于3D预培训的努力经常依赖于自我监督的方法，它在未标记的数据上使用预测或对比学习来构建不变的3D表示。然而，由于大规模监督信息的不可用，从这些学习框架获得语义不变和歧视性表示仍然存在问题。在本文中，我们重新审视了一种创新但简单的完全监督的3D网络预训练框架，以利用来自大型2D自然图像数据集的语义监督。通过重新设计的3D网络架构，重新设计的自然图像用于解决数据稀缺问题并开发强大的3D表示。四个基准数据集上的综合实验表明，所提出的预先接受的模型可以有效地加速收敛，同时还提高了各种3D医学成像任务，例如分类，分割和检测的准确性。此外，与从头划伤的训练相比，它可以节省高达60％的注释工作。在NIH Deeplesion数据集上，它同样地实现了最先进的检测性能，优于早期的自我监督和完全监督的预训练方法，以及从头训练进行培训的方法。为了促进3D医疗模型的进一步发展，我们的代码和预先接受的模型权重在https://github.com/urmagicsmine/cspr上公开使用。

预训练为深入学习支持的X线射线分析中最近的成功奠定了基础。它通过在源域上进行大规模完全监督或自我监督的学习来学习可转移的图像表示。然而，监督的预培训需要复杂和劳动密集的两级人类辅助注释过程，而自我监督的学习不能与监督范例竞争。为了解决这些问题，我们提出了一个跨监督的方法，命名为审查监督（指的）的自由文本报告，该报告从射线照相中获取来自原始放射学报告的自由监督信号。该方法采用了视觉变压器，旨在从每个患者研究中的多种视图中学习联合表示。在极其有限的监督下，引用其在4个众所周知的X射线数据集上的转移学习和自我监督学习对应。此外，甚至是基于具有人辅助结构标签的射线照相的源区的甚至超越方法。因此，有可能取代规范的预训练方法。

使用卷积神经网络，面部属性（例如，年龄和吸引力）估算性能得到了大大提高。然而，现有方法在培训目标和评估度量之间存在不一致，因此它们可能是次优。此外，这些方法始终采用具有大量参数的图像分类或面部识别模型，其携带昂贵的计算成本和存储开销。在本文中，我们首先分析了两种最新方法（排名CNN和DLDL）之间的基本关系，并表明排名方法实际上是隐含的学习标签分布。因此，该结果首先将两个现有的最新方法统一到DLDL框架中。其次，为了减轻不一致和降低资源消耗，我们设计了一种轻量级网络架构，并提出了一个统一的框架，可以共同学习面部属性分发和回归属性值。在面部年龄和吸引力估算任务中都证明了我们的方法的有效性。我们的方法使用单一模型实现新的最先进的结果，使用36美元\倍，参数减少3美元，在面部年龄/吸引力估算上的推动速度为3美元。此外，即使参数的数量进一步降低到0.9m（3.8MB磁盘存储），我们的方法也可以实现与最先进的结果。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.