Whole-slide image (WSI) classification is a challenging task because 1)
patches from WSI lack annotation, and 2) WSI possesses unnecessary variability,
e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made
significant progress, allowing for classification based on slide-level, rather
than patch-level, annotations. However, existing MIL methods ignore that all
patches from normal slides are normal. Using this free annotation, we introduce
a semi-supervision signal to de-bias the inter-slide variability and to capture
the common factors of variation within normal patches. Because our method is
orthogonal to the MIL algorithm, we evaluate our method on top of the recently
proposed MIL algorithms and also compare the performance with other
semi-supervised approaches. We evaluate our method on two public WSI datasets
including Camelyon-16 and TCGA lung cancer and demonstrate that our approach
significantly improves the predictive performance of existing MIL algorithms
and outperforms other semi-supervised algorithms. We release our code at
this https URL

整张切片图像分类中，使用多实例学习和半监督方法来改善基于切片级注释的分类算法，通过在正常切片上引入自由标注信号来减少切片间的变异性并捕捉常见变异因素，该方法显著提高了预测性能并优于其他半监督算法。

用于全幻灯片图像分类的紧凑、无偏负实例嵌入

Compact and De-biased Negative Instance Embedding for Multi-Instance  Learning on Whole-Slide Image Classification

Entity alignment (EA) plays an important role in automatically integrating
knowledge graphs (KGs) from multiple sources. Recent approaches based on Graph
Neural Network (GNN) obtain entity representation from relation information and
have achieved promising results. Besides, more and more methods introduce
semi-supervision to ask for more labeled training data. However, two challenges
still exist in GNN-based EA methods: (1) Deeper GNN Encoder: The GNN encoder of
current methods has limited depth (usually 2-layers). (2) Low-quality
Bootstrapping: The generated semi-supervised data is of low quality. In this
paper, we propose a novel framework, Echo Entity Alignment (EchoEA), which
leverages 4-levels self-attention mechanism to spread entity information to
relations and echo back to entities. Furthermore, we propose attribute-combined
bi-directional global-filtered strategy (ABGS) to improve bootstrapping, reduce
false samples and generate high-quality training data. The experimental results
on three real-world cross-lingual datasets are stable at around 96\% at hits@1
on average, showing that our approach not only significantly outperforms the
state-of-the-art GNN-based methods, but also is universal and transferable for
existing EA methods.

本篇论文提出了一种基于 Echo 实体对齐的新框架，该框架利用 4 级自注意机制将实体信息传播到关系中并回传到实体，同时提出了属性组合双向全局过滤策略以改进引导和生成高质量训练数据，实验结果表明，此方法在三个现实跨语言数据集上的表现稳定在平均 hits@1 上达到 96%，不仅显著优于现有 GNN-based 方法，还是现有 EA 方法的通用和可转移的。

EchoEA: 实体对齐中的实体和关系之间的回声信息

EchoEA: Echo Information between Entities and Relations for Entity  Alignment

Predicting how events induce emotions in the characters of a story is
typically seen as a standard multi-label classification task, which usually
treats labels as anonymous classes to predict. They ignore information that may
be conveyed by the emotion labels themselves. We propose that the semantics of
emotion labels can guide a model's attention when representing the input story.
Further, we observe that the emotions evoked by an event are often related: an
event that evokes joy is unlikely to also evoke sadness. In this work, we
explicitly model label classes via label embeddings, and add mechanisms that
track label-label correlations both during training and inference. We also
introduce a new semi-supervision strategy that regularizes for the correlations
on unlabeled data. Our empirical evaluations show that modeling label semantics
yields consistent benefits, and we advance the state-of-the-art on an emotion
inference task.

本论文提出一种基于标签语义的情感标注模型，包括标签 embedding 和半监督策略，并在情感推断任务方面取得了领先研究成果。