深入学习模型遭受较旧阶段中课程的灾难性遗忘，因为它们在类增量学习设置中新阶段所引入的课程中受过培训。在这项工作中，我们表明灾难性忘记对模型预测的影响随着相同图像的方向的变化而变化，这是一种新的发现。基于此，我们提出了一种新的数据集合方法，该方法结合了图像的不同取向的预测，以帮助模型保留关于先前所见的类别的进一步信息，从而减少忘记模型预测的效果。但是，如果使用传统技术训练，我们无法直接使用数据集合方法。因此，我们还提出了一种新的双重增量学习框架，涉及共同培训网络，其中包括两个增量学习目标，即类渐进式学习目标以及我们提出的数据增量学习目标。在双增量学习框架中，每个图像属于两个类，即图像类（用于类增量学习）和方向类（用于数据增量学习）。在Class-Incremental学习中，每个新阶段都会引入一组新的类，并且模型无法从较旧阶段访问完整的培训数据。在我们提出的数据增量学习中，方向类在所有阶段保持相同，并且在类 - 增量学习中的新阶段引入的数据充当了这些方向类的新培训数据。我们经验证明双增量学习框架对数据集合方法至关重要。我们将拟议的课程逐步增量学习方法应用拟议方法，并经验表明我们的框架显着提高了这些方法的性能。

大多数人工智能（AI）研究都集中在高收入国家，其中成像数据，IT基础设施和临床专业知识丰富。但是，在需要医学成像的有限资源环境中取得了较慢的进步。例如，在撒哈拉以南非洲，由于获得产前筛查的机会有限，围产期死亡率的率很高。在这些国家，可以实施AI模型，以帮助临床医生获得胎儿超声平面以诊断胎儿异常。到目前为止，已经提出了深度学习模型来识别标准的胎儿平面，但是没有证据表明它们能够概括获得高端超声设备和数据的中心。这项工作研究了不同的策略，以减少在高资源临床中心训练并转移到新的低资源中心的胎儿平面分类模型的域转移效果。为此，首先在丹麦的一个新中心对1,008例患者的新中心进行评估，接受了1,008名患者的新中心，后来对五个非洲中心（埃及，阿尔及利亚，乌干达，加纳和马拉维进行了相同的表现），首先在丹麦的一个新中心进行评估。 ）每个患者有25名。结果表明，转移学习方法可以是将小型非洲样本与发达国家现有的大规模数据库相结合的解决方案。特别是，该模型可以通过将召回率提高到0.92 \ pm 0.04 $，同时又可以维持高精度。该框架显示了在临床中心构建可概括的新AI模型的希望，该模型在具有挑战性和异质条件下获得的数据有限，并呼吁进行进一步的研究，以开发用于资源较少的国家 /地区的AI可用性的新解决方案。

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.

Can a neural network estimate an object's dimension in the wild? In this paper, we propose a method and deep learning architecture to estimate the dimensions of a quadrilateral object of interest in videos using a monocular camera. The proposed technique does not use camera calibration or handcrafted geometric features; however, features are learned with the help of coefficients of a segmentation neural network during the training process. A real-time instance segmentation-based Deep Neural Network with a ResNet50 backbone is employed, giving the object's prototype mask and thus provides a region of interest to regress its dimensions. The instance segmentation network is trained to look at only the nearest object of interest. The regression is performed using an MLP head which looks only at the mask coefficients of the bounding box detector head and the prototype segmentation mask. We trained the system with three different random cameras achieving 22% MAPE for the test dataset for the dimension estimation

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.

Pruning refers to the elimination of trivial weights from neural networks. The sub-networks within an overparameterized model produced after pruning are often called Lottery tickets. This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network. We introduce a novel winning ticket called Cyclic Overlapping Lottery Ticket (COLT) by data splitting and cyclic retraining of the pruned network from scratch. We apply a cyclic pruning algorithm that keeps only the overlapping weights of different pruned models trained on different data segments. Our results demonstrate that COLT can achieve similar accuracies (obtained by the unpruned model) while maintaining high sparsities. We show that the accuracy of COLT is on par with the winning tickets of Lottery Ticket Hypothesis (LTH) and, at times, is better. Moreover, COLTs can be generated using fewer iterations than tickets generated by the popular Iterative Magnitude Pruning (IMP) method. In addition, we also notice COLTs generated on large datasets can be transferred to small ones without compromising performance, demonstrating its generalizing capability. We conduct all our experiments on Cifar-10, Cifar-100 & TinyImageNet datasets and report superior performance than the state-of-the-art methods.

Automatic medical image classification is a very important field where the use of AI has the potential to have a real social impact. However, there are still many challenges that act as obstacles to making practically effective solutions. One of those is the fact that most of the medical imaging datasets have a class imbalance problem. This leads to the fact that existing AI techniques, particularly neural network-based deep-learning methodologies, often perform poorly in such scenarios. Thus this makes this area an interesting and active research focus for researchers. In this study, we propose a novel loss function to train neural network models to mitigate this critical issue in this important field. Through rigorous experiments on three independently collected datasets of three different medical imaging domains, we empirically show that our proposed loss function consistently performs well with an improvement between 2%-10% macro f1 when compared to the baseline models. We hope that our work will precipitate new research toward a more generalized approach to medical image classification.

Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.