Jim Martin

Veena Kaustaban, Qinle Ba, Ipshita Bhattacharya et al.

Recent years have seen great advancements in the development of deep learning models for histopathology image analysis in digital pathology applications, evidenced by the increasingly common deployment of these models in both research and clinical settings. Although such models have shown unprecedented performance in solving fundamental computational tasks in DP applications, they suffer from catastrophic forgetting when adapted to unseen data with transfer learning. With an increasing need for deep learning models to handle ever changing data distributions, including evolving patient population and new diagnosis assays, continual learning models that alleviate model forgetting need to be introduced in DP based analysis. However, to our best knowledge, there is no systematic study of such models for DP-specific applications. Here, we propose CL scenarios in DP settings, where histopathology image data from different sources/distributions arrive sequentially, the knowledge of which is integrated into a single model without training all the data from scratch. We then established an augmented dataset for colorectal cancer H&E classification to simulate shifts of image appearance and evaluated CL model performance in the proposed CL scenarios. We leveraged a breast tumor H&E dataset along with the colorectal cancer to evaluate CL from different tumor types. In addition, we evaluated CL methods in an online few-shot setting under the constraints of annotation and computational resources. We revealed promising results of CL in DP applications, potentially paving the way for application of these methods in clinical practice.

Michael Elrod, Niloufar Mehrabi, Rahul Amin et al.

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveillance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particularly when prior information is not available. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task execution. Our approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient information aggregation and decision-making under constrained communication. A transformer-based message-passing mechanism, augmented with edge-feature-enhanced attention, captures complex interaction patterns, while a Double Deep Q-Network (Double DQN) with prioritized experience replay optimizes agent policies in partially observable environments. This integration is carefully designed to address specific requirements of multi-agent navigation, such as scalability, adaptability, and efficient task execution. Experimental results demonstrate superior performance, with 90% service provisioning and 100% grid coverage (node discovery), while reducing the average steps per episode to 200, compared to 600 for benchmark methods such as particle swarm optimization (PSO), greedy algorithms and DQN.

Satarupa Mukherjee, Jim Martin, Yao Nie

Multiplex brightfield imaging offers the advantage of simultaneously analyzing multiple biomarkers on a single slide, as opposed to single biomarker labeling on multiple consecutive slides. To accurately analyze multiple biomarkers localized at the same cellular compartment, two representative biomarker sets were selected as assay models - cMET-PDL1-EGFR and CD8-LAG3-PDL1, where all three biomarkers can co-localize on the cell membrane. One of the most crucial preliminary stages for analyzing such assay is identifying each unique chromogen on individual cells. This is a challenging problem due to the co-localization of membrane stains from all the three biomarkers. It requires advanced color unmixing for creating the equivalent singleplex images from each triplex image for each biomarker. In this project, we developed a cycle-Generative Adversarial Network (cycle-GAN) method for unmixing the triplex images generated from the above-mentioned assays. Three different models were designed to generate the singleplex image for each of the three stains Tamra (purple), QM-Dabsyl (yellow) and Green. A notable novelty of our approach was that the input to the network were images in the optical density domain instead of conventionally used RGB images. The use of the optical density domain helped in reducing the blurriness of the synthetic singleplex images, which was often observed when the network was trained on RGB images. The cycle-GAN models were validated on 10,800 lung, gastric and colon images for the cMET-PDL1-EGFR assay and 3600 colon images for the CD8-LAG3-PDL1 assay. Visual as well as quantified assessments demonstrated that the proposed method is effective and efficient when compared with the manual reviewing results and is readily applicable to various multiplex assays.

Manveen Kaur, Anjan Rayamajhi, Mizanur Rahman et al.

Cooperative Adaptive Cruise Control (CACC) is a pivotal vehicular application that would allow transportation field to achieve its goals of increased traffic throughput and roadway capacity. This application is of paramount interest to the vehicular technology community with a large body of literature dedicated to research within different aspects of CACC, including but not limited to security with CACC. Of all available literature, the overwhelming focus in on CACC utilizing vehicle-to-vehicle (V2V) communication. In this work, we assert that a qualitative increase in vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) involvement has the potential to add greater value to CACC. In this study, we developed a strategy for detection of a denial-of-service (DoS) attack on a CACC platoon where the system edge in the vehicular network plays a central role in attack detection. The proposed security strategy is substantiated with a simulation-based evaluation using the ns-3 discrete event network simulator. Empirical evidence obtained through simulation-based results illustrate successful detection of the DoS attack at four different levels of attack severity using this security strategy.