Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.

Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary substantially due to environment changes and sensor noises, but deep models only know the training domain style. Such domain style gap impedes model generalization on diverse real-world domains. Our proposed Normalization Perturbation (NP) can effectively overcome this domain style overfitting problem. We observe that this problem is mainly caused by the biased distribution of low-level features learned in shallow CNN layers. Thus, we propose to perturb the channel statistics of source domain features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training. We further explore the style-sensitive channels for effective style synthesis. Normalization Perturbation only relies on a single source domain and is surprisingly effective and extremely easy to implement. Extensive experiments verify the effectiveness of our method for generalizing models under real-world domain shifts.

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset.

2D低剂量单板腹部计算机断层扫描（CT）切片可直接测量身体成分，这对于对衰老的健康关系进行定量表征至关重要。然而，由于不同年内获得的纵向切片之间的位置方差，使用2D腹部切片对人体成分变化的纵向分析具有挑战性。为了减少位置差异，我们将条件生成模型扩展到我们的C-斜肌，该模型在腹部区域进行任意轴向切片作为条件，并通过估计潜在空间的结构变化来生成定义的椎骨水平切片。对来自内部数据集的1170名受试者的实验和BTCV Miccai挑战赛的50名受试者的实验表明，我们的模型可以从现实主义和相似性方面产生高质量的图像。来自巴尔的摩纵向研究（BLSA）数据集的20名受试者的外部实验，其中包含纵向单腹部切片验证了我们的方法可以在肌肉和内脏脂肪面积方面与切片的位置方差进行协调。我们的方法提供了一个有希望的方向，将切片从不同的椎骨水平映射到目标切片，以减少单个切片纵向分析的位置差异。源代码可在以下网址获得：https：//github.com/masilab/c-slicegen。

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realize global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissues structures. Inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting anatomies of 133 structures in brain, 14 organs in abdomen, 4 hierarchical components in kidney, and inter-connected kidney tumors). We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in single network, outperforms prior state-of-the-art method SLANT27 ensembled with 27 network tiles, our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively.

从心脏病学到神经病学的疾病中，代谢健康越来越多地成为危险因素，身体成分的效率评估对于定量表征这些关系至关重要。 2D低剂量单切层扫描术（CT）提供了高分辨率，定量组织图，尽管视野有限。尽管在量化图像上下文时已经提出了许多潜在的分析，但尚无对低剂量单切片CT纵向变异性进行自动分割的全面研究。我们使用受监督的基于深度学习的细分和无监督的聚类方法研究了1469个巴尔的摩纵向研究（BLSA）腹部数据集的1469名纵向研究（BLSA）腹部数据集的1816片。在前两次扫描中有两年差距的1469名受试者中有300名被选出，以评估纵向变异性，其中包括类内相关系数（ICC）和变异系数（CV），以组织/器官的大小和平均强度为单位。我们表明，我们的分割方法在纵向环境中是稳定的，骰子范围为13个目标腹部组织结构的0.821至0.962。我们观察到ICC <0.5的大多数器官的较高变异性，肌肉，腹壁，脂肪和体膜的变化较低，平均ICC> 0.8。我们发现器官的变异性与2D切片的横截面位置高度相关。我们的努力铺平了定量探索和质量控制，以减少纵向分析中的不确定性。

语言的演变遵循逐渐变化的规则。语法，词汇和词汇语义转移会随着时间的推移而发生，导致了直觉的语言差距。因此，用不同的时代语言编写了大量文本，这为自然语言处理任务（例如单词分割和机器翻译）造成了障碍。尽管中文历史悠久，但以前的中国自然语言处理研究主要集中在特定时代的任务上。因此，我们为中文单词分割（CWS）提出了一个跨时代的学习框架，该框架使用开关记忆（SM）模块来合并ERA特定的语言知识。来自不同时代的四个语料库的实验表明，每个语料库的性能都显着提高。进一步的分析还表明，SM可以有效地将时代的知识整合到神经网络中。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

已经开发了许多基于深度学习的方法，用于用于H＆E图像的核分割，并已接近人类的表现。但是，将这种方法直接应用于另一种图像方式，例如免疫组织化学（IHC）图像，可能无法实现令人满意的性能。因此，我们开发了一种基于生成的对抗网络（GAN）方法，以将IHC图像转换为H＆E图像，同时保留核位置和形态，然后将预训练的核分割模型应用于虚拟H＆E图像。我们证明了所提出的方法比几种基线方法更好地工作，包括直接应用对H＆E培训的细胞核分割方法，例如Cellpose和Hover-Net，并使用两个公共IHC图像数据集进行了培训。

分布式概括（OOD）都是关于对环境变化的学习不变性。如果每个类中的上下文分布均匀分布，则OOD将是微不足道的，因为由于基本原则，可以轻松地删除上下文：类是上下文不变的。但是，收集这种平衡的数据集是不切实际的。学习不平衡的数据使模型偏见对上下文，从而伤害了OOD。因此，OOD的关键是上下文平衡。我们认为，在先前工作中广泛采用的假设，可以直接从偏见的类预测中注释或估算上下文偏差，从而使上下文不完整甚至不正确。相比之下，我们指出了上述原则的另一面：上下文对于类也不变，这激励我们将类（已经被标记为已标记的）视为不同环境以解决上下文偏见（没有上下文标签）。我们通过最大程度地减少阶级样本相似性的对比损失，同时确保这种相似性在所有类别中不变，从而实现这一想法。在具有各种上下文偏见和域间隙的基准测试中，我们表明，配备了我们上下文估计的简单基于重新加权的分类器实现了最新的性能。我们在https://github.com/simpleshinobu/irmcon上提供了附录中的理论理由和代码。