我们提出了一种新型的半监督学习方法，用于分类组织病理学图像。我们采用贴片级注释以及新颖的共同训练损失的强大监督，以创建半监督的学习框架。共同培训依赖于多种有条件独立且充分的数据视图。我们使用颜色反卷积在病理图像中分离苏木精和曙红通道，从而创建每个幻灯片的两个视图，这些视图可以部分满足这些要求。两个单独的CNN用于将两个视图嵌入关节特征空间中。我们在此功能空间中使用对比的损失来实施共同训练。我们在清晰的细胞肾细胞和前列腺癌中评估了我们的方法，并证明了对最先进的半监督学习方法的改善。

我们对最近的自我和半监督ML技术进行严格的评估，从而利用未标记的数据来改善下游任务绩效，以河床分割的三个遥感任务，陆地覆盖映射和洪水映射。这些方法对于遥感任务特别有价值，因为易于访问未标记的图像，并获得地面真理标签通常可以昂贵。当未标记的图像（标记数据集之外）提供培训时，我们量化性能改进可以对这些遥感分割任务进行期望。我们还设计实验以测试这些技术的有效性，当测试集相对于训练和验证集具有域移位时。

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings.

这项工作提出了一种新型的自我监督的预训练方法，以学习有效的表示，而没有在组织病理学医学图像上使用放大倍率的因素进行标签。其他最先进的工作主要集中在完全监督的学习方法上，这些学习方法严重依赖人类注释。但是，标记和未标记数据的稀缺性是组织病理学的长期挑战。当前，没有标签的表示学习仍未探索组织病理学领域。提出的方法是放大事先的对比相似性（MPC），可以通过利用放大倍率，电感转移和减少人类先验的宽度乳腺癌数据集中的无标签来进行自我监督的学习。当仅20％的标签用于微调和表现以前的工作中，在完全监督的学习环境中，该方法与恶性分类的最新学习相匹配。它提出了一个假设，并提供了经验证据来支持，从而减少人类优先导致自学​​中有效表示学习。这项工作的实施可在github-https：//github.com/prakashchhipa/magnification-prior-self-supervised-method上在线获得。

在过去几年中，无监督的学习取得了很大的进展，特别是通过对比的自我监督学习。用于基准测试自我监督学习的主导数据集已经想象，最近的方法正在接近通过完全监督培训实现的性能。然而，ImageNet DataSet在很大程度上是以对象为中心的，并且目前尚不清楚这些方法的广泛不同的数据集和任务，这些方法是非以对象为中心的，例如数字病理学。虽然自我监督的学习已经开始在这个领域探讨了令人鼓舞的结果，但有理由看起来更接近这个环境与自然图像和想象成的不同。在本文中，我们对组织病理学进行了对比学学习的深入分析，引脚指向对比物镜的表现如何不同，由于组织病理学数据的特征。我们提出了一些考虑因素，例如对比目标和超参数调整的观点。在大量的实验中，我们分析了组织分类的下游性能如何受到这些考虑因素的影响。结果指出了对比学习如何减少数字病理中的注释工作，但需要考虑特定的数据集特征。为了充分利用对比学习目标，需要不同的视野和超参数校准。我们的结果为实现组织病理学应用的自我监督学习的全部潜力铺平了道路。

长期以来，半监督学习（SSL）已被证明是一种有限的标签模型的有效技术。在现有的文献中，基于一致性的基于正则化的方法，这些方法迫使扰动样本具有类似的预测，而原始的样本则引起了极大的关注。但是，我们观察到，当标签变得极为有限时，例如，每个类别的2或3标签时，此类方法的性能会大大降低。我们的实证研究发现，主要问题在于语义信息在数据增强过程中的漂移。当提供足够的监督时，可以缓解问题。但是，如果几乎没有指导，错误的正则化将误导网络并破坏算法的性能。为了解决该问题，我们（1）提出了一种基于插值的方法来构建更可靠的正样品对； （2）设计一种新颖的对比损失，以指导学习网络的嵌入以在样品之间进行线性更改，从而通过扩大保证金决策边界来提高网络的歧视能力。由于未引入破坏性正则化，因此我们提出的算法的性能在很大程度上得到了改善。具体而言，所提出的算法的表现优于第二好算法（COMATT），而当CIFAR-10数据集中的每个类只有两个标签可用时，可以实现88.73％的分类精度，占5.3％。此外，我们通过通过我们提出的策略大大改善现有最新算法的性能，进一步证明了所提出的方法的普遍性。

Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.

半监督学习方法已成为对打击获得大量注释数据的挑战的活跃研究领域。为了提高半监督学习方法表现的目标，我们提出了一种新颖的框架，Hiematch，一种半监督方法，利用分层信息来降低标签成本并表现以及vanilla半监督学习方法。分层信息通常是具有细粒标签的粗标签（例如，啄木鸟）的粗标签（例如，啄木鸟）的现有知识（例如，柔软的啄木鸟或金朝啄木鸟）。但是，尚未探讨使用使用粗类标签来改进半监督技术的监督。在没有细粒度的标签的情况下，Himatch利用标签层次结构，并使用粗级标签作为弱监控信号。此外，Himatch是一种改进任何半熟的学习框架的通用方法，我们使用我们的结果在最近的最先进的技术Mixmatch和Fixmatch上展示了这一点。我们评估了在两个基准数据集，即CiFar-100和Nabirds上的Himatch疗效。与MixMatch相比，HOMACHACT可以在CIFAR-100上减少50％的粒度标签50％的用量，仅在前1个精度的边缘下降0.59％。代码：https://github.com/07agarg/hiermatch.

Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel size) and complex (made of up to millions of cells). The current state-of-the-art (SoTA) approach to classify WSI subdivides them into tiles, encodes them by pre-trained networks and applies Multiple Instance Learning (MIL) to train for specific downstream tasks. However, annotated datasets are often small, typically a few hundred to a few thousand WSI, which may cause overfitting and underperforming models. Conversely, the number of unannotated WSI is ever increasing, with datasets of tens of thousands (soon to be millions) of images available. While it has been previously proposed to use these unannotated data to identify suitable tile representations by self-supervised learning (SSL), downstream classification tasks still require full supervision because parts of the MIL architecture is not trained during tile level SSL pre-training. Here, we propose a strategy of slide level SSL to leverage the large number of WSI without annotations to infer powerful slide representations. Applying our method to The Cancer-Genome Atlas, one of the most widely used data resources in cancer research (16 TB image data), we are able to downsize the dataset to 23 MB without any loss in predictive power: we show that a linear classifier trained on top of these embeddings maintains or improves previous SoTA performances on various benchmark WSI classification tasks. Finally, we observe that training a classifier on these representations with tiny datasets (e.g. 50 slides) improved performances over SoTA by an average of +6.3 AUC points over all downstream tasks.

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weaklyaugmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -just 4 labels per class. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. The code is available at https://github.com/google-research/fixmatch.

This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive selfsupervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by Sim-CLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-ofthe-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100× fewer labels. 1

Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.

我们提出了Parse，这是一种新颖的半监督结构，用于学习强大的脑电图表现以进行情感识别。为了减少大量未标记数据与标记数据有限的潜在分布不匹配，Parse使用成对表示对准。首先，我们的模型执行数据增强，然后标签猜测大量原始和增强的未标记数据。然后将其锐化的标签和标记数据的凸组合锐化。最后，进行表示对准和情感分类。为了严格测试我们的模型，我们将解析与我们实施并适应脑电图学习的几种最先进的半监督方法进行了比较。我们对四个基于公共EEG的情绪识别数据集，种子，种子IV，种子V和Amigos（价和唤醒）进行这些实验。该实验表明，我们提出的框架在种子，种子-IV和Amigos（Valence）中的标记样品有限的情况下，取得了总体最佳效果，同时接近种子V和Amigos中的总体最佳结果（达到第二好） （唤醒）。分析表明，我们的成对表示对齐方式通过减少未标记数据和标记数据之间的分布比对来大大提高性能，尤其是当每类仅1个样本被标记时。

This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S 4 L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S 4 L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels.

数据标签通常是开发计算病理模型时最具挑战性的任务。病理学家参与是生成准确的标签，以及对病理学家的局限性和大型标有数据集的需求已经导致使用患者级标签，机器辅助注释和主动学习在内的地区研究了弱监督学习。在本文中，我们探索自我监督的学习，以减少计算病理中的标记负担。我们在使用Barlow Twiss方法对乳腺癌组织进行分类的背景下探讨这一点，我们将自我监督与低数据场景中的预先训练网络中的替代品进行了比较。对于本文探索的任务，我们发现想象成预先训练的网络在很大程度上超越了使用巴洛双胞胎获得的自我监督的表示。

完全监督分类的问题是，它需要大量的注释数据，但是，在许多数据集中，很大一部分数据是未标记的。为了缓解此问题，半监督学习（SSL）利用了标记域上的分类器知识，并将其推送到无标记的域，该域具有与注释数据相似的分布。 SSL方法的最新成功至关重要地取决于阈值伪标记，从而对未标记的域的一致性正则化。但是，现有方法并未在训练过程中纳入伪标签或未标记样品的不确定性，这是由于嘈杂的标签或由于强大的增强而导致的分布样品。受SSL最近发展的启发，我们本文的目标是提出一个新颖的无监督不确定性意识的目标，依赖于核心和认识论不确定性量化。通过提出的不确定性感知损失功能，我们的方法优于标准SSL基准，在计算轻量级的同时，与最新的方法相匹配，或与最先进的方法相提并论。我们的结果优于复杂数据集（例如CIFAR-100和MINI-IMAGENET）的最新结果。

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that SSL algorithms would face in real-world applications. After creating a unified reimplementation of various widely-used SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and performance can degrade substantially when the unlabeled dataset contains out-ofdistribution examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available. 2 * Equal contribution 2 https://github.com/brain-research/realistic-ssl-evaluation 32nd Conference on Neural Information Processing Systems (NeurIPS 2018),

监管基于深度学习的方法，产生医学图像分割的准确结果。但是，它们需要大量标记的数据集，并获得它们是一种艰苦的任务，需要临床专业知识。基于半/自我监督的学习方法通​​过利用未标记的数据以及有限的注释数据来解决此限制。最近的自我监督学习方法使用对比损失来从未标记的图像中学习良好的全球层面表示，并在像想象网那样的流行自然图像数据集上实现高性能。在诸如分段的像素级预测任务中，对于学习良好的本地级别表示以及全局表示来说至关重要，以实现更好的准确性。然而，现有的局部对比损失的方法的影响仍然是学习良好本地表现的限制，因为类似于随机增强和空间接近定义了类似和不同的局部区域;由于半/自我监督设置缺乏大规模专家注释，而不是基于当地地区的语义标签。在本文中，我们提出了局部对比损失，以便通过利用从未标记的图像的未标记图像的伪标签获得的语义标签信息来学习用于分割的良好像素级别特征。特别地，我们定义了建议的损失，以鼓励具有相同伪标签/标签的像素的类似表示，同时与数据集中的不同伪标签/标签的像素的表示。我们通过联合优化标记和未标记的集合和仅限于标记集的分割损失，通过联合优化拟议的对比损失来进行基于伪标签的自培训和培训网络。我们在三个公共心脏和前列腺数据集上进行了评估，并获得高分割性能。

近年来，随着深度神经网络方法的普及，手术计算机视觉领域经历了相当大的突破。但是，用于培训的标准全面监督方法需要大量的带注释的数据，从而实现高昂的成本；特别是在临床领域。已经开始在一般计算机视觉社区中获得吸引力的自我监督学习（SSL）方法代表了对这些注释成本的潜在解决方案，从而使仅从未标记的数据中学习有用的表示形式。尽管如此，SSL方法在更复杂和有影响力的领域（例如医学和手术）中的有效性仍然有限且未开发。在这项工作中，我们通过在手术计算机视觉的背景下研究了四种最先进的SSL方法（Moco V2，Simclr，Dino，SWAV），以解决这一关键需求。我们对这些方法在cholec80数据集上的性能进行了广泛的分析，以在手术环境理解，相位识别和工具存在检测中为两个基本和流行的任务。我们检查了它们的参数化，然后在半监督设置中相对于训练数据数量的行为。如本工作所述和进行的那样，将这些方法的正确转移到手术中，可以使SSL的一般用途获得可观的性能 - 相位识别率高达7％，而在工具存在检测方面，则具有20％ - 半监督相位识别方法高达14％。该代码将在https://github.com/camma-public/selfsupsurg上提供。

半监督学习（SSL）是规避建立高性能模型的昂贵标签成本的最有前途的范例之一。大多数现有的SSL方法常规假定标记和未标记的数据是从相同（类）分布中绘制的。但是，在实践中，未标记的数据可能包括课外样本；那些不能从标签数据中的封闭类中的单热编码标签，即未标记的数据是开放设置。在本文中，我们介绍了Opencos，这是一种基于最新的自我监督视觉表示学习框架来处理这种现实的半监督学习方案。具体而言，我们首先观察到，可以通过自我监督的对比度学习有效地识别开放式未标记数据集中的类外样本。然后，Opencos利用此信息来克服现有的最新半监督方法中的故障模式，通过利用一式旋转伪标签和软标签来为已识别的识别和外部未标记的标签数据分别。我们广泛的实验结果表明了Opencos的有效性，可以修复最新的半监督方法，适合涉及开放式无标记数据的各种情况。