Dung T. Tran

Dung T. Tran, Hung Vu, Anh Tran et al.

This paper introduces SEMISE, a novel method for representation learning in medical imaging that combines self-supervised and supervised learning. By leveraging both labeled and augmented data, SEMISE addresses the challenge of data scarcity and enhances the encoder's ability to extract meaningful features. This integrated approach leads to more informative representations, improving performance on downstream tasks. As result, our approach achieved a 12% improvement in classification and a 3% improvement in segmentation, outperforming existing methods. These results demonstrate the potential of SIMESE to advance medical image analysis and offer more accurate solutions for healthcare applications, particularly in contexts where labeled data is limited.

Cuong Nguyen, Dung T. Tran, Hong Nguyen et al.

In real-world traffic surveillance, vehicle images captured under adverse weather, poor lighting, or high-speed motion often suffer from severe noise and blur. Such degradations significantly reduce the accuracy of license plate recognition systems, especially when the plate occupies only a small region within the full vehicle image. Restoring these degraded images a fast realtime manner is thus a crucial pre-processing step to enhance recognition performance. In this work, we propose a Vertical Residual Autoencoder (VRAE) architecture designed for the image enhancement task in traffic surveillance. The method incorporates an enhancement strategy that employs an auxiliary block, which injects input-aware features at each encoding stage to guide the representation learning process, enabling better general information preservation throughout the network compared to conventional autoencoders. Experiments on a vehicle image dataset with visible license plates demonstrate that our method consistently outperforms Autoencoder (AE), Generative Adversarial Network (GAN), and Flow-Based (FB) approaches. Compared with AE at the same depth, it improves PSNR by about 20%, reduces NMSE by around 50%, and enhances SSIM by 1%, while requiring only a marginal increase of roughly 1% in parameters.

Dung T. Tran, Nguyen B. Ha, Van-Dinh Nguyen et al.

Federated learning (FL) is a promising approach for addressing scalability and latency issues in large-scale networks by enabling collaborative model training without requiring the sharing of raw data. However, existing FL frameworks often overlook the computational heterogeneity of edge clients and the growing training burden on resource-limited devices. However, FL suffers from high communication costs and complex model aggregation, especially with large models. Previous works combine split learning (SL) and hierarchical FL (HierFL) to reduce device-side computation and improve scalability, but this introduces training complexity due to coordination across tiers. To address these issues, we propose SHeRL-FL, which integrates SL and hierarchical model aggregation and incorporates representation learning at intermediate layers. By allowing clients and edge servers to compute training objectives independently of the cloud, SHeRL-FL significantly reduces both coordination complexity and communication overhead. To evaluate the effectiveness and efficiency of SHeRL-FL, we performed experiments on image classification tasks using CIFAR-10, CIFAR-100, and HAM10000 with AlexNet, ResNet-18, and ResNet-50 in both IID and non-IID settings. In addition, we evaluate performance on image segmentation tasks using the ISIC-2018 dataset with a ResNet-50-based U-Net. Experimental results demonstrate that SHeRL-FL reduces data transmission by over 90\% compared to centralized FL and HierFL, and by 50\% compared to SplitFed, which is a hybrid of FL and SL, and further improves hierarchical split learning methods.