Dense prediction tasks such as segmentation and detection of pathological entities hold crucial clinical value in the digital pathology workflow. However, obtaining dense annotations on large cohorts is usually tedious and expensive. Contrastive learning (CL) is thus often employed to leverage large volumes of unlabeled data to pre-train the backbone network. To boost CL for dense prediction, some studies have proposed variations of dense matching objectives in pre-training. However, our analysis shows that employing existing dense matching strategies on histopathology images enforces invariance among incorrect pairs of dense features and, thus, is imprecise. To address this, we propose a precise location-based matching mechanism that utilizes the overlapping information between geometric transformations to precisely match regions in two augmentations. Extensive experiments on two pretraining datasets (TCGA-BRCA, NCT-CRC-HE) and three downstream datasets (GlaS, CRAG, BCSS) highlight the superiority of our method in semantic and instance segmentation tasks. Our method outperforms previous dense matching methods by up to 7.2 % in average precision for detection and 5.6 % in average precision for instance segmentation tasks. Additionally, by using our matching mechanism in the three popular contrastive learning frameworks, MoCo-v2, VICRegL and ConCL, the average precision in detection is improved by 0.7 % to 5.2 % and the average precision in segmentation is improved by 0.7 % to 4.0 %, demonstrating its generalizability.

In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis. Image-to-image translation networks such as CycleGANs are in principle capable of learning an artifact removal function from unpaired data. However, we identify a surjection problem with artifact removal, and propose an weakly-supervised extension to CycleGAN to address this. We assemble a pan-cancer dataset comprising artifact and clean tiles from the TCGA database. Promising results highlight the soundness of our method.

在整个幻灯片成像中，基于苏木精和曙红（H＆E）（H＆E）和免疫组织化学（IHC）的常用染色技术染色了组织景观的不同方面。在检测转移的情况下，IHC提供了一个独特的读数，病理学家很容易解释。但是，IHC是一种更昂贵的方法，在所有医疗中心都不可用。因此，使用深层神经网络从H＆E生成IHC图像成为一种有吸引力的替代方法。诸如Cyclegans之类的深层生成模型学习两个图像域之间的语义一致映射，同时模拟每个域的纹理特性。因此，它们是污渍转移应用程序的合适选择。但是，它们仍然完全无监督，并且没有在染色转移中执行生物学一致性的机制。在本文中，我们提出了以歧视者区域形式向自行车行驶的扩展。这使Cyclegan可以从未配对的数据集中学习，此外，还希望对象有部分注释，希望它能强制执行一致性。我们在整个幻灯片图像上介绍了用例，其中IHC染色为转移细胞提供了实验生成的信号。我们证明了我们的方法优于先前的艺术在两个数据集上对组织病理学瓷砖的污渍转移中的优越性。我们的代码和型号可在https://github.com/jcboyd/miccai2022-Roigan上找到。

组织病理学全幻灯片图像（WSIS）在临床研究中起着非常重要的作用，并作为许多癌症诊断的黄金标准。但是，由于其巨大尺寸，生成用于处理WSIS的自动工具是具有挑战性的。当前，为了解决这个问题，传统方法依靠多个实例学习（MIL）策略来处理贴剂级别的WSI。尽管有效，但这种方法在计算上很昂贵，因为将WSI整理成斑块需要时间，并且不探索这些瓷砖之间的空间关系。为了解决这些限制，我们提出了一个本地监督的学习框架，该框架通过探索包含的整个本地和全球信息来处理整个幻灯片。该框架将预训练的网络划分为几个模块，并使用辅助模型在本地优化每个模块。我们还引入了一个随机特征重建单元（RFR），以在训练过程中保留区分特征，并将方法的性能提高1％至3％。对三个公开可用的WSI数据集进行了广泛的实验：TCGA-NSCLC，TCGA-RCC和LKS，突出了我们方法在不同分类任务上的优越性。我们的方法的准确性优于最先进的MIL方法，而高7至10倍。此外，将其分为八个模块时，我们的方法需要端到端培训所需的GPU总内存总数的20％。我们的代码可从https://github.com/cvlab-stonybrook/local_learning_wsi获得。

组织病理学全幻灯片图像（WSIS）可以显示出明显的院间变异性，例如照明，颜色或光学伪影。这些变化是由在医疗中心（染色，扫描仪）中使用不同扫描协议引起的，可能会严重损害看不见的协议上的算法概括。这激发了开发新方法以限制这种表现的下降。在本文中，为了增强对看不见的目标协议的鲁棒性，我们提出了基于多域图像到图像翻译的新测试时间数据增强。它允许在对每个源域进行分类并结合预测之前将图像从看不见的协议投射到每个源域。该测试时间增强方法可显着增强域概括的性能。为了证明其有效性，我们的方法已在两项不同的组织病理学任务上进行了评估，在这些任务中，它的表现优于常规域的概括，标准的H＆E特定颜色增强/归一化和标准测试时间增强技术。我们的代码可在https://gitlab.com/vitadx/articles/test time-i2i-translation-semembling上公开获取。

迄今为止，迄今为止，众所周知，对广泛的互补临床相关任务进行了全面比较了医学图像登记方法。这限制了采用研究进展，以防止竞争方法的公平基准。在过去五年内已经探讨了许多新的学习方法，但优化，建筑或度量战略的问题非常适合仍然是开放的。 Learn2reg涵盖了广泛的解剖学：脑，腹部和胸部，方式：超声波，CT，MRI，群体：患者内部和患者内部和监督水平。我们为3D注册的培训和验证建立了较低的入境障碍，这帮助我们从20多个独特的团队中汇编了65多个单独的方法提交的结果。我们的互补度量集，包括稳健性，准确性，合理性和速度，使得能够独特地位了解当前的医学图像登记现状。进一步分析监督问题的转移性，偏见和重要性，主要是基于深度学习的方法的优越性，并将新的研究方向开放到利用GPU加速的常规优化的混合方法。

可变形的注册包括找到两个不同图像之间的最佳密集对应。许多算法已发表，但临床应用难以解决优化问题所需的高计算时间。通过利用GPU计算和学习过程，深入学习超越了这种限制。然而，许多深度学习方法不考虑经典算法尊重的理想性质。在本文中，我们呈现MICS，一种用于医学成像注册的新型深度学习算法。由于注册是一个不良问题，我们将我们的算法集中在不同性质的方面：逆一致性，对称性和方向节约。我们还将我们的算法与多步策略组合以改进和改进变形网格。虽然许多方法向脑MRI应用了登记，但我们探讨了更具挑战性的身体定位：腹部CT。最后，我们在Learn2Reg挑战期间使用的数据集中评估了我们的方法，允许与已发布的方法进行公平比较。

Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies. This distribution of tasks can be specified by the curriculum. A curriculum is meant to improve the results of learning and accelerate it. We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning, where a task sequence is created based on the success rate of each task. In this setting, each task is an algorithmically created environment instance with a unique configuration. The algorithm selects the order of tasks that provide the fastest learning for agents. The probability of selecting any of the tasks for the next stage of learning is determined by evaluating its performance score in previous stages. Experiments were carried out in the Partially Observable Grid Environment for Multiple Agents (POGEMA) and Procgen benchmark. We demonstrate that SITP matches or surpasses the results of other curriculum design methods. Our method can be implemented with handful of minor modifications to any standard RL framework and provides useful prioritization with minimal computational overhead.

This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team "nigula" obtained second place according to manual evaluation by the organizers.

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.