Deep learning can extract rich data representations if provided sufficient quantities of labeled training data. For many tasks however, annotating data has significant costs in terms of time and money, owing to the high standards of subject matter expertise required, for example in medical and geophysical image interpretation tasks. Active Learning can identify the most informative training examples for the interpreter to train, leading to higher efficiency. We propose an Active learning method based on jointly learning representations for supervised and unsupervised tasks. The learned manifold structure is later utilized to identify informative training samples most dissimilar from the learned manifold from the error profiles on the unsupervised task. We verify the efficiency of the proposed method on a seismic facies segmentation dataset from the Netherlands F3 block survey, significantly outperforming contemporary methods to achieve the highest mean Intersection-Over-Union value of 0.773.

Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. AI has the capability to automate the process but its adoption has been limited thus far owing to a lack of transparency in the way complicated, black box models generate decisions. We demonstrate how LIME -- a model-agnostic explanation technique -- can be used to inject trust in model decisions by uncovering the model's reasoning process for individual predictions. It generates these explanations by fitting interpretable models in the local neighborhood of specific datapoints being queried. On a dataset of well outcomes and corresponding geophysical attribute data, we show how LIME can induce trust in model's decisions by revealing the decision-making process to be aligned to domain knowledge. Further, it has the potential to debug mispredictions made due to anomalous patterns in the data or faulty training datasets.

Humans exhibit disagreement during data labeling. We term this disagreement as human label uncertainty. In this work, we study the ramifications of human label uncertainty (HLU). Our evaluation of existing uncertainty estimation algorithms, with the presence of HLU, indicates the limitations of existing uncertainty metrics and algorithms themselves in response to HLU. Meanwhile, we observe undue effects in predictive uncertainty and generalizability. To mitigate the undue effects, we introduce a novel natural scene statistics (NSS) based label dilution training scheme without requiring massive human labels. Specifically, we first select a subset of samples with low perceptual quality ranked by statistical regularities of images. We then assign separate labels to each sample in this subset to obtain a training set with diluted labels. Our experiments and analysis demonstrate that training with NSS-based label dilution alleviates the undue effects caused by HLU.

This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the medical field, there exists a variety of labels for data that serve different purposes at different stages of a diagnostic and treatment process. Clinical labels and biomarker labels are two examples. In general, clinical labels are easier to obtain in larger quantities because they are regularly collected during routine clinical care, while biomarker labels require expert analysis and interpretation to obtain. Within the field of ophthalmology, previous work has shown that clinical values exhibit correlations with biomarker structures that manifest within optical coherence tomography (OCT) scans. We exploit this relationship between clinical and biomarker data to improve performance for biomarker classification. This is accomplished by leveraging the larger amount of clinical data as pseudo-labels for our data without biomarker labels in order to choose positive and negative instances for training a backbone network with a supervised contrastive loss. In this way, a backbone network learns a representation space that aligns with the clinical data distribution available. Afterwards, we fine-tune the network trained in this manner with the smaller amount of biomarker labeled data with a cross-entropy loss in order to classify these key indicators of disease directly from OCT scans. Our method is shown to outperform state of the art self-supervised methods by as much as 5% in terms of accuracy on individual biomarker detection.

眼睛的临床诊断是对多种数据模式进行的，包括标量临床标签，矢量化生物标志物，二维底面图像和三维光学相干性层析成像（OCT）扫描。临床从业者使用所有可用的数据模式来诊断和治疗糖尿病性视网膜病（DR）或糖尿病黄斑水肿（DME）等眼部疾病。在眼科医学领域启用机器学习算法的使用需要研究治疗期内所有相关数据之间的关系和相互作用。现有的数据集受到限制，因为它们既不提供数据，也没有考虑数据模式之间的显式关系建模。在本文中，我们介绍了用于研究以上限制的视觉眼睛语义（橄榄）数据集的眼科标签。这是第一个OCT和近IIR眼底数据集，其中包括临床标签，生物标记标签，疾病标签和时间序列的患者治疗信息，来自相关临床试验。该数据集由1268个近红外图像组成，每个图像至少具有49个10月扫描和16个生物标志物，以及4个临床标签和DR或DME的疾病诊断。总共有96张眼睛的数据在至少两年的时间内平均，每只眼睛平均治疗66周和7次注射。我们在医学图像分析中为橄榄数据集进行了橄榄数据集的实用性，并为核心和新兴机器学习范式提供了基准和具体研究方向。

在本文中，我们在神经网络的决策过程中提倡两个阶段。首先是现有的进纸推理框架，其中感知给定数据中的模式并与先前学习的模式相关联。第二阶段是一个较慢的反射阶段，我们要求网络通过考虑和评估所有可用选择来反思其前馈决策。一起，我们将这两个阶段称为内省学习。我们使用训练有素的神经网络的梯度来测量这种反射。简单的三层多层感知器被用作基于所有提取梯度特征预测的第二阶段。我们感知地从两个阶段可视化事后解释，以提供内省的视觉接地。对于识别的应用，我们表明内省网络在推广到噪声数据时，内省网络的稳健性更高，容易校准错误的42％。我们还说明了内省网络在下游任务中的价值，这些任务需要普遍性和校准，包括主动学习，分布外检测和不确定性估计。最后，我们将提议的机器内省为人类内省，以应用图像质量评估。

本文认为，从医学角度来看，主动学习更加明智。实际上，疾病在患者队列中以不同的形式表现出来。现有的框架主要使用数学结构来设计基于不确定性或基于多样性的方法来选择最有用的样本。但是，这种算法并不能自然地表现出医疗界和医疗保健提供者的使用。因此，如果有的话，它们在临床环境中的部署非常有限。为此，我们提出了一个框架，将临床见解纳入了可以与现有算法合并的主动学习样本选择过程中。我们可解释的主动学习框架捕获了患者的多种疾病表现，以提高OCT分类的泛化表现。经过全面的实验，我们报告说，将患者洞察力纳入活跃的学习框架中，可以产生匹配或超过两个架构上的五个常用范式，其中一个数据集具有患者分布不平衡的数据集。此外，该框架将其集成到现有的医疗实践中，因此可以由医疗保健提供者使用。

我们建议利用梯度检测对抗和分布样品。我们介绍了混杂标签（与训练过程中的正常标签不同），以探测神经网络的有效表达性。梯度描述了模型正确表示给定输入所需的变化量，从而洞悉了网络体系结构属性建立的模型的代表力以及培训数据。通过引入不同设计的标签，我们消除了对推理期间梯度生成的地面真相标签的依赖。我们表明，我们的基于梯度的方法可以根据模型的有效表达性捕获异常，而没有超参数调整或其他处理，并且优于对抗和分布检测的最先进方法。

用于图像分类任务的神经网络假设推理期间的任何给定图像属于其中一个培训类别。在模型可能遇到未知类别的输入的现实应用程序中，这种封闭式假设受到挑战。开放式识别旨在通过正确对已知类别进行分类，通过拒绝未知类来解决此问题。在本文中，我们建议利用从已知分类器获得的基于梯度的表示，以训练仅使用已知类别实例的未知检测器。渐变对应于正确表示给定样本所需的模型更新量，我们利用该模型更新以了解模型具有其学术功能的输入的能力。我们的方法可以使用以有监督的方式对已知类别进行培训的任何分类器使用，而无需明确对未知样本的分布进行建模。我们表明，基于梯度的方法在开放式分类中优于最先进的方法高达11.6％。

在地震解释中，各种岩石结构的像素级标签可能耗时且获得昂贵。结果，通常存在一系列非平凡的未标记数据，这些数据仅仅是因为传统的深度学习方法依赖于完全标记的卷。为了纠正这个问题，已经提出了使用自我监督的方法来从未标记的数据中学习有用的表示形式。但是，传统的对比学习方法是基于从自然图像领域的假设，这些假设不利用地震环境。为了将这种环境纳入对比学习中，我们提出了一种基于切片在地震量中的位置的新型积极选择策略。我们表明，在语义分割任务中，从我们的方法表现出的学术表现形式超出了艺术对比的学习方法。