病理系统地诱导形态学变化，从而提供了主要但不足以量化的可观察到诊断来源。该研究基于计算机断层扫描（CT）体积的形态特征（3D形态学）开发了病理状态的预测模型。开发了一个完整的工作流程，以进行网状提取和简化器官表面的工作流程，并与平均曲率和网状能的分布自动提取形态特征自动提取。然后对XGBoost监督分类器进行了训练和测试，以预测病理状态。该框架应用于肺结节恶性肿瘤的预测。在具有恶性肿瘤的NLST数据库的子集中，仅使用3D形态学证实了活检，将肺结节的分类模型分类为恶性与良性AUC的良性0.964。 （1）临床相关特征的其他三组经典特征经过训练和测试，AUC为0.58，（2）111辐射因子学的AUC为0.976，（3）含有结节大小，衰减和衰减和衰减的放射科医生地面真相（GT） Spiculation定性注释的AUC为0.979。我们还测试了Brock模型并获得0.826的AUC。将3D形态学和放射素学特征结合在一起，可以实现最新的结果，而AUC为0.978，其中3D形态学具有一些最高的预测能力。作为对公共独立队列的验证，将模型应用于LIDC数据集，3D形态学的AUC达到0.906，而3D型物体+放射线学则获得了0.958的AUC，在挑战中排名第二。它将曲率分布确定为预测肺结核恶性肿瘤的有效特征，并可以直接应用于任意计算机辅助诊断任务。

在多任务学习（MTL）中，通过优化目标函数来训练多任务网络是一种常见的做法，这是特定于任务的目标函数的加权平均值。尽管该策略的计算优势很明显，但在文献中尚未研究所得损失格局的复杂性。可以说，它的优化可能比对特定于任务的目标的单独优化更加困难。在这项工作中，我们通过在不同特定于任务的目标函数上交替进行独立的梯度下降步骤来研究这种替代方案的好处，并制定了一种新颖的方式，将这种方法与最先进的优化者相结合。由于特定于任务的目标的分离是以增加计算时间为代价的，因此我们提出了一个随机的任务分组，作为更好的优化和计算效率之间的权衡。与平均目标函数和其他最先进的MTL方法相比，三个众所周知的视觉MTL数据集的实验结果在损失和标准指标上显示出更好的总体绝对性能。特别是，我们的方法在处理不同性质的任务时显示出最大的好处，并且可以更广泛地探索共享参数空间。我们还表明，我们的随机分组策略允许在这些好处和计算效率之间进行权衡。

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iteratively improves it by searching a large neighborhood around the current best solution. LNS relies on heuristics to select neighborhoods to search in. In this paper, we focus on designing effective and efficient heuristics in LNS for integer linear programs (ILP) since a wide range of COPs can be represented as ILPs. Local Branching (LB) is a heuristic that selects the neighborhood that leads to the largest improvement over the current solution in each iteration of LNS. LB is often slow since it needs to solve an ILP of the same size as input. Our proposed heuristics, LB-RELAX and its variants, use the linear programming relaxation of LB to select neighborhoods. Empirically, LB-RELAX and its variants compute as effective neighborhoods as LB but run faster. They achieve state-of-the-art anytime performance on several ILP benchmarks.

Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild.

在许多情况下，更简单的模型比更复杂的模型更可取，并且该模型复杂性的控制是机器学习中许多方法的目标，例如正则化，高参数调整和体系结构设计。在深度学习中，很难理解复杂性控制的潜在机制，因为许多传统措施并不适合深度神经网络。在这里，我们开发了几何复杂性的概念，该概念是使用离散的dirichlet能量计算的模型函数变异性的量度。使用理论论据和经验结果的结合，我们表明，许多常见的训练启发式方法，例如参数规范正规化，光谱规范正则化，平稳性正则化，隐式梯度正则化，噪声正则化和参数初始化的选择，都可以控制几何学复杂性，并提供一个统一的框架，以表征深度学习模型的行为。

自动分割前庭造型瘤（VS）和来自磁共振成像（MRI）的耳蜗可以促进与治疗计划。无监督的分割方法已显示出令人鼓舞的结果，而无需耗时且费力的手动标记过程。在本文中，我们提出了一种在无监督域的适应设置中进行VS和耳蜗分割的方法。具体而言，我们首先开发了跨站点的跨模式未配对的图像翻译策略，以丰富合成数据的多样性。然后，我们设计了一种基于规则的离线增强技术，以进一步最大程度地减少域间隙。最后，我们采用一个自我训练的自我配置分割框架，以获得最终结果。在Crossmoda 2022验证排行榜上，我们的方法已获得竞争性与耳蜗细分性能，平均骰子得分为0.8178 $ \ pm $ 0.0803和0.8433 $ \ pm $ 0.0293。

在过去的十年中，我们看到了工业数据，计算能力的巨大改善以及机器学习的重大理论进步。这为在大规模非线性监控和控制问题上使用现代机器学习工具提供了机会。本文对过程行业的应用进行了对最新结果的调查。

模拟逼真的传感器是自主系统数据生成的挑战，通常涉及精心手工的传感器设计，场景属性和物理建模。为了减轻这一点，我们引入了一条管道，用于对逼真的激光雷达传感器进行数据驱动的模拟。我们提出了一个模型，该模型可以在RGB图像和相应的LIDAR功能（例如Raydrop或每点强度）之间直接从真实数据集中进行映射。我们表明，我们的模型可以学会编码逼真的效果，例如透明表面上的掉落点或反射材料上的高强度回报。当应用于现成的模拟器软件提供的天真播放点云时，我们的模型通过根据场景的外观预测强度和删除点来增强数据，以匹配真实的激光雷达传感器。我们使用我们的技术来学习两个不同的LIDAR传感器的模型，并使用它们相应地改善模拟的LiDAR数据。通过车辆细分的示例任务，我们表明通过我们的技术增强模拟点云可以改善下游任务性能。