With the increasing availability of diverse healthcare data sources, such as medical images and electronic health records, there is a growing need to effectively integrate and fuse this multimodal data for comprehensive analysis and decision-making. However, despite its potential, multimodal data fusion in healthcare remains limited. This review paper provides an overview of existing literature on multimodal data fusion in healthcare, covering 69 relevant works published between 2018 and 2024. It focuses on methodologies that integrate different data types to enhance medical analysis, including techniques for integrating medical images with structured and unstructured data, combining multiple image modalities, and other features. Additionally, the paper reviews various approaches to multimodal data fusion, such as early, intermediate, and late fusion methods, and examines the challenges and limitations associated with these techniques. The potential benefits and applications of multimodal data fusion in various diseases are highlighted, illustrating specific strategies employed in healthcare artificial intelligence (AI) model development. This research synthesizes existing information to facilitate progress in using multimodal data for improved medical diagnosis and treatment planning.

Background: Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.

Autonomous driving technology has advanced significantly with deep learning, but noise and attacks threaten its real-world deployment. While research has revealed vulnerabilities in individual intelligent tasks, a comprehensive evaluation of these impacts across complete end-to-end systems is still underexplored. To address this void, we thoroughly analyze the robustness of four end-to-end autonomous driving systems against various noise and build the RobustE2E Benchmark, including five traditional adversarial attacks and a newly proposed Module-Wise Attack specifically targeting end-to-end autonomous driving in white-box settings, as well as four major categories of natural corruptions (a total of 17 types, with five severity levels) in black-box settings. Additionally, we extend the robustness evaluation from the open-loop model level to the closed-loop case studies of autonomous driving system level. Our comprehensive evaluation and analysis provide valuable insights into the robustness of end-to-end autonomous driving, which may offer potential guidance for targeted improvements to models. For example, (1) even the most advanced end-to-end models suffer large planning failures under minor perturbations, with perception tasks showing the most substantial decline; (2) among adversarial attacks, our Module-Wise Attack poses the greatest threat to end-to-end autonomous driving models, while PGD-$l2$ is the weakest, and among four categories of natural corruptions, noise and weather are the most harmful, followed by blur and digital distortion being less severe; (3) the integrated, multitask approach results in significantly higher robustness and reliability compared with the simpler design, highlighting the critical role of collaborative multitask in autonomous driving; and (4) the autonomous driving systems amplify the model’s lack of robustness, etc. Our research contributes to developing more resilient autonomous driving models and their deployment in the real world.

Artificial intelligence (AI) server systems, including AI servers and AI server clusters, are widely utilized in AI applications. The performance of an AI server system determines the performance of the performance an AI application, which has garnered significant attentions and investments from industries and users. However, performance is influenced not only by the AI computing accelerating chips but also by the other configurations of an AI server system, such as architecture, memory, bus, central processing unit (CPU), interconnect equipment, software, etc. As these components are often provided by different vendors, the myriad combinations thereof present challenges in assessing performance in an architecture-neural, reproducible, fairness-enhanced, and performance bottleneck identification-oriented manner. In response to this need, this paper introduces AISBench, a performance benchmark for AI server systems. AISBench comprises standardized rules and a test toolkit that has been agreed upon by over 20 AI server system and server component manufacturers. Compared to other AI performance benchmarks, AISBench provides a more comprehensive metrics system that enables performance benchmarking as well as identification of performance bottlenecks for optimization. Experimental data indicate that our benchmark testing approach offers sufficient comprehensiveness, effectiveness, and stability.

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.