医学图像分割模型的性能指标用于衡量参考注释和预测之间的一致性。在开发此类模型中,使用了一组通用指标,以使结果更具可比性。但是,公共数据集中的分布与临床实践中遇到的案例之间存在不匹配。许多常见的指标无法衡量这种不匹配的影响,尤其是对于包含不确定,小或空参考注释的临床数据集。因此,可能无法通过此类指标来验证模型在临床上有意义的一致性。评估临床价值的维度包括独立于参考注释量的大小,考虑参考注释的不确定性,体积计和/或位置一致性的奖励以及对空参考注释正确分类的奖励。与普通的公共数据集不同,我们的内部数据集更具代表性。它包含不确定的,小或空的参考注释。我们研究了有关深度学习框架的预测的公开度量指标,以确定哪些设置共同指标可提供有意义的结果。我们将公共基准数据集进行比较而没有不确定,小或空参考注释。该代码将发布。
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自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是,当前的度量使用通常是不明智的,并且不能反映基本的域名。在这里,我们提出了一个全面的框架,该框架指导研究人员以问题意识的方式选择绩效指标。具体而言,我们专注于生物医学图像分析问题,这些问题可以解释为图像,对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ,数据集和算法与输出问题相关的属性的属性与问题指纹相关,同时还将其映射到适当的问题类别,即图像级分类,语义分段,实例,实例细分或对象检测。然后,它指导用户选择和应用一组适当的验证指标的过程,同时使他们意识到与个人选择相关的潜在陷阱。在本文中,我们描述了指标重新加载推荐框架的当前状态,目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的,将在社区驱动的优化之后公开作为用户友好的工具包提供。
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放射线学使用定量医学成像特征来预测临床结果。目前,在新的临床应用中,必须通过启发式试验和纠正过程手动完成各种可用选项的最佳放射组方法。在这项研究中,我们提出了一个框架,以自动优化每个应用程序的放射线工作流程的构建。为此,我们将放射线学作为模块化工作流程,并为每个组件包含大量的常见算法。为了优化每个应用程序的工作流程,我们使用随机搜索和结合使用自动化机器学习。我们在十二个不同的临床应用中评估我们的方法,从而在曲线下导致以下区域:1)脂肪肉瘤(0.83); 2)脱粘型纤维瘤病(0.82); 3)原发性肝肿瘤(0.80); 4)胃肠道肿瘤(0.77); 5)结直肠肝转移(0.61); 6)黑色素瘤转移(0.45); 7)肝细胞癌(0.75); 8)肠系膜纤维化(0.80); 9)前列腺癌(0.72); 10)神经胶质瘤(0.71); 11)阿尔茨海默氏病(0.87);和12)头颈癌(0.84)。我们表明,我们的框架具有比较人类专家的竞争性能,优于放射线基线,并且表现相似或优于贝叶斯优化和更高级的合奏方法。最后,我们的方法完全自动优化了放射线工作流的构建,从而简化了在新应用程序中对放射线生物标志物的搜索。为了促进可重复性和未来的研究,我们公开发布了六个数据集,框架的软件实施以及重现这项研究的代码。
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尽管自动图像分析的重要性不断增加,但最近的元研究揭示了有关算法验证的主要缺陷。性能指标对于使用的自动算法的有意义,客观和透明的性能评估和验证尤其是关键,但是在使用特定的指标进行给定的图像分析任务时,对实际陷阱的关注相对较少。这些通常与(1)无视固有的度量属性,例如在存在类不平衡或小目标结构的情况下的行为,(2)无视固有的数据集属性,例如测试的非独立性案例和(3)无视指标应反映的实际生物医学领域的兴趣。该动态文档的目的是说明图像分析领域通常应用的性能指标的重要局限性。在这种情况下,它重点介绍了可以用作图像级分类,语义分割,实例分割或对象检测任务的生物医学图像分析问题。当前版本是基于由全球60多家机构的国际图像分析专家进行的关于指标的Delphi流程。
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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
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Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
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Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
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Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
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KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
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Kirilov et al (2019) develop a metric, called Panoptic Quality (PQ), to evaluate image segmentation methods. The metric is based on a confusion table, and compares a predicted to a ground truth segmentation. The only non straightforward part in this comparison is to align the segments in the two segmentations. A metric only works well if that alignment is a partial bijection. Kirilov et al (2019) list 3 desirable properties for a definition of alignment: it should be simple, interpretable and effectively computable. There are many definitions guaranteeing a partial bijection and these 3 properties. We present the weakest: one that is both sufficient and necessary to guarantee that the alignment is a partial bijection. This new condition is effectively computable and natural. It simply says that the number of correctly predicted elements (in image segmentation, the pixels) should be larger than the number of missed, and larger than the number of spurious elements. This is strictly weaker than the proposal in Kirilov et al (2019). In formulas, instead of |TP|> |FN\textbar| + |FP|, the weaker condition requires that |TP|> |FN| and |TP| > |FP|. We evaluate the new alignment condition theoretically and empirically.
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