目的:(1)开发深度学习算法,以识别3D光学相干断层扫描(OCT)扫描中的视神经头(ONH)的主要组织结构; (2)利用这些信息在健康,光盘博森(奇数)和乳头膜ONHS之间鲁棒地区分。由于高颅内压(51只眼)和健康对照(100只眼睛),这是一种横截面对比研究,由于高颅内压(51只眼睛),以及健康的对照(100只眼)。使用OCT获得ONH的3D扫描,然后加工以改善深层组织可见性。首先,使用984 B-Scans(从130只眼睛)开发了深度学习算法,以识别:主要的神经/结缔组织和奇数区域。使用骰子系数(DC)评估我们的算法的性能。在第2步骤中,使用1500Ct卷设计了一个分类算法(随机林),以严格从其德鲁森和普拉拉马那肿胀得分(来自细分)来执行3级分类(1:奇数,2:Papilledema,3:健康) )。为了评估性能,我们报告了每个类的接收器操作特征曲线(AUC)下的区域。我们的分割算法能够在存在时隔离神经和结缔组织和奇数区域。这是在测试集上的平均DC为0.93 $ 0.03的平均直流,相应于良好性能。分类是用高AUC的分类,即检测奇数,0.99美元0.01 0.01美元,用于检测Papilledema的0.99美元,0.98美元$ 0.02用于检测健康的ONH。我们的AI方法可以使用单个OCT扫描来准确地歧视奇数乳头。我们的分类表现非常出色,有需要在更大的人口中验证。我们的方法可能有可能建立10月作为神经眼科诊断成像的主干。
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$ \ mathbf {perive} $:使用人工智能(AI)到:(1)从相对较大的人群中利用视神经头(ONH)的生物力学知识; (2)评估ONH的单个光学相干断层扫描(OCT)扫描的稳健性; (3)确定哪些关键的三维(3D)结构特征使给定的ONH稳健。 $ \ Mathbf {Design} $:回顾性横断面研究。 $ \ mathbf {Methods} $:316个受试者通过Ophthalmo-Dynamometry在急性眼内和之后与OCT成像。然后将IOP诱导的椎板胶状变形映射为3D,并用于对ONH进行分类。 LC变形高于4%的人被认为是脆弱的,而变形较低的人则较低4%。从这些数据中学习,我们比较了三种AI算法,以严格地从基线(未呈现的)OCT卷中预测鲁棒性:(1)随机森林分类器; (2)自动编码器; (3)动态图CNN(DGCNN)。后一种算法还使我们能够确定哪些关键的3D结构特征使给定的智能稳定。 $ \ mathbf {结果} $:所有3种方法都能够单独预测3D结构信息的稳健性,而无需执行生物力学测试。 DGCNN(接收器操作曲线下的区域[AUC]:0.76 $ \ pm $ 0.08)的表现优于自动编码器(AUC:0.70 $ \ pm $ 0.07)和随机森林分类器(AUC:0.69 $ \ pm $ 0.05)。有趣的是,为了评估稳健性,DGCNN主要使用了巩膜和LC插入部位的信息。 $ \ mathbf {结论} $:我们提出了一种AI驱动的方法,可以仅从ONH的单个OCT扫描中评估给定ONH的稳健性,而无需进行生物力学测试。纵向研究应确定ONH鲁棒性是否可以帮助我们确定快速的视野损失进展者。
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本文与离散观察到的非线性扩散过程的在线过滤有关。我们的方法基于完全适应的辅助粒子滤波器,该滤芯涉及DOOB的$ h $转换通常是棘手的。我们提出了一个计算框架,通过使用非线性FEYNMAN-KAC公式和神经网络求解基础的落后Kolmogorov方程来近似这些$ H $转换。该方法允许在数据鉴别过程之前训练本地最佳的粒子过滤器。数值实验表明,在高度信息观察结果的制度中,当观测值在模型下极端,如果状态维度很大时,所提出的方法可以比引导粒子滤波器更有效的数量级。
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在这项工作中,研究了来自磁共振图像的脑年龄预测的深度学习技术,旨在帮助鉴定天然老化过程的生物标志物。生物标志物的鉴定可用于检测早期神经变性过程,以及预测与年龄相关或与非年龄相关的认知下降。在这项工作中实施并比较了两种技术:应用于体积图像的3D卷积神经网络和应用于从轴向平面的切片的2D卷积神经网络,随后融合各个预测。通过2D模型获得的最佳结果,其达到了3.83年的平均绝对误差。 - Neste Trabalho S \〜AO InvestigaDAS T \'Ecnicas de Aprendizado Profundo Para a previ \ c {c} \〜ate daade脑电站a partir de imagens de resson \ ^ ancia magn \'etica,Visando辅助Na Identifica \ c {C} \〜AO de BioMarcadores Do Processo Natural de Envelhecimento。一个identifica \ c {c} \〜ao de bioMarcarcores \'e \'util para a detec \ c {c} \〜ao de um processo neurodegenerativo em Est \'Agio无数,Al \'em de possibilitar Prever Um decl 'inio cognitivo relacionado ou n \〜ao \`一个懒惰。 Duas T \'ECICAS S \〜AO ImportyAdas E Comparadas Teste Trabalho:Uma Rede神经卷应3D APLICADA NA IMAGEM VOLUM \'ETRICA E UME REDE神经卷轴2D APLICADA A FATIAS DO PANIAS轴向,COM后面fus \〜AO DAS PREDI \ C {c} \ \ oes个人。 o Melhor ResultAdo Foi optido Pelo Modelo 2D,Que Alcan \ C {C} OU UM ERRO M \'EDIO ABSOLUTO DE 3.83 ANOS。
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目的:评估中央视网膜血管躯干及其分支(CRVT&B)的三维(3D)结构构型是否可用作青光眼的诊断标志物。方法:我们训练了深度学习网络,从光神经头(ONH)的光学相干断层扫描(OCT)体积的B-Scans自动分割CRVT&B。随后,使用从OCT体积中提取的CRVT&B的结构构型,两种不同的方法用于青光眼诊断。在第一种方法中,我们旨在仅使用CNN的3D CNN和CRVT&B的3D结构提供诊断。对于第二种方法,我们将CRVT&B的3D结构投射到三个平面上以获得2D图像,然后使用2D CNN进行诊断。使用骰子系数评估分割精度,而使用接收器操作特性曲线(AUC)下的区域评估诊断准确度。 CRVT&B的诊断性能也与视网膜神经纤维层(RNFL)厚度进行了比较。结果:我们的分割网络能够从OCT扫描有效地分段视网膜血管。在测试集上,我们实现了0.81 \ PM0.07的骰子系数。 3D和2D诊断网络能够将青光眼与非青光眼受试者区分别分别区分82.7%和83.3%的精度。 CRVT&B的相应AUC为0.89和0.90,高于用RNFL厚度获得的0.90℃。结论:我们的工作表明,CRVT&B的诊断功能优于金标 - 标准的青光眼参数,即RNFL厚度。我们的作品还建议主要视网膜血管形成骨架 - 其配置可以代表主要的ONH结构变化,通常观察到青光眼的开发和进展。
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已知深入学习方法遭受校准问题:通常会产生过度自信的估计。这些问题在低数据制度中加剧了。虽然研究了概率模型的校准,但在低数据制度中校准了极其过度参数化模型,呈现出独特的挑战。我们表明深度集合并不一定导致改进的校准特性。事实上,我们表明标准合奏方法,与混合规则化等现代技术结合使用时,可以导致校准的型号更少。本文审查了在数据稀缺时利用深度学习的三种最简单和常用方法之间的相互作用:数据增强,合奏和后处理校准方法。虽然标准合奏技术肯定有助于提高准确性,但我们证明了深度融合的校准依赖于微妙的折衷。我们还发现,随着深度合并使用时,需要稍微调整校准方法,如温度缩放,并且粗略地,需要在平均过程之后执行。我们的模拟表明,与低数据制度中的标准深度集合相比,这种简单的策略可以在一系列基准分类问题上对预期的校准误差(ECE)进行比较。
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Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a F1-score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.
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Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.
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Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.
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Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.
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