最近的一些研究描述了深层卷积神经网络，以诊断与人类专家相似甚至卓越表现的乳腺癌乳腺癌。最好的技术之一可以进行两种转移学习：第一个使用在自然图像上训练的模型来创建“补丁分类器”，该模型将小型子图表分类；第二个使用补丁分类器来扫描整个乳房X线照片并创建“单视图全图分类器”。我们建议进行第三次转移学习，以获取“两视图分类器”，以使用两种乳房X线摄影视图：双侧颅颅和中外侧倾斜。我们使用效率网络作为模型的基础。我们使用CBIS-DDSM数据集“端到端”训练整个系统。为了确保统计鲁棒性，我们使用以下方式两次测试系统，（a）5倍交叉验证； （b）数据集的原始培训/测试部门。我们的技术使用5倍的交叉验证达到0.9344的AUC（在ROC的误差率相等的误差率下，准确性，灵敏度和特异性为85.13％）。据我们所知，使用原始的数据集除法，我们的技术达到了0.8483，尽管我们知道的最高的AUC在此问题上，尽管每项工作的测试条件上的细微差异不允许进行准确的比较。推理代码和模型可在https://github.com/dpetrini/two-views-classifier上获得

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

开发了一个3D深度学习模型（OARNet）并用于在CT图像上描绘28 H＆N OAR。 OARNET利用密集连接的网络来检测OAR边界盒，然后在盒子内划定OAR。它将来自任何层的信息重用到后续层，并使用跳过连接来组合来自不同密集块电平的信息来逐步提高描绘精度。培训最多使用最多28名专家手册划定（MD）桨从165 CTS划算。骰子相似度系数（DSC）和第95百分位HAUSDORFF距离（HD95）相对于MD评估了70个其他CT。对MD的平均值，最大和根平均方形剂量差异评估了70cts的56个。 oarnet与UANET，ANATOMYNET和MULTI-ATLAS分段（MAS）进行比较。使用95％置信区间的Wilcoxon签名级别测试用于评估意义。 Wilcoxon签署了排名测试表明，与UANET相比，OARNET改善了（P <0.05）DSC（23/28桨）和HD95（17/28）。 OARNet优于DSC（28/28）和HD95（27/28）的Anatomynet和MAS。与UANET相比，OARNET将中位数DSC改善至0.05和HD95，高达1.5mm。与Anatomynet和MAS相比，OARNET将中位数（DSC，HD95）改为高达（0.08,2.7mm）和（0.17,6.3mm）。 DoSimetry，Oarnet优于Uanet（Dmax 7/28; Dmean 10/28），Anatomynet（Dmax 21/28; Dmean 24/28）和MAS（Dmax 22/28; Dmean 21/28）。 DenSenet架构使用混合方法进行优化，该混合方法执行OAR特定的边界框检测，然后是要素识别。与其他自动描绘方法相比，Oarnet优于或等于所有几何（颞叶L，HD95）和28 H＆N OAR的一个剂量（眼睛L，平均剂量）终点，并且优于或者等于所有OAR的Anatomynet和MAS。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial data and sequences in general. The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models. Such expectations appear naturally in several learning contexts, such as likelihood estimation (MLE) and Markov score climbing (MSC). PARIS has linear computational complexity, limited memory requirements and comes with non-asymptotic bounds, convergence results and stability guarantees. Still, being based on self-normalised importance sampling, the PaRIS estimator is biased. Our first contribution is to design a novel additive smoothing algorithm, the Parisian particle Gibbs PPG sampler, which can be viewed as a PaRIS algorithm driven by conditional SMC moves, resulting in bias-reduced estimates of the targeted quantities. We substantiate the PPG algorithm with theoretical results, including new bounds on bias and variance as well as deviation inequalities. Our second contribution is to apply PPG in a learning framework, covering MLE and MSC as special examples. In this context, we establish, under standard assumptions, non-asymptotic bounds highlighting the value of bias reduction and the implicit Rao--Blackwellization of PPG. These are the first non-asymptotic results of this kind in this setting. We illustrate our theoretical results with numerical experiments supporting our claims.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

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.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.