Huy Hoang Nguyen

Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala et al.

Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two agents -- a radiologist and a general practitioner -- we predict prognosis with two transformer-based components that share information with each other. The first transformer in this framework aims to analyze the imaging data, and the second one leverages its internal states as inputs, also fusing them with auxiliary clinical data. The temporal nature of the problem is modeled within the transformer states, allowing us to treat the forecasting problem as a multi-task classification, for which we propose a novel loss. We show the effectiveness of our approach in predicting the development of structural knee osteoarthritis changes and forecasting Alzheimer's disease clinical status directly from raw multi-modal data. The proposed method outperforms multiple state-of-the-art baselines with respect to performance and calibration, both of which are needed for real-world applications. An open-source implementation of our method is made publicly available at \url{https://github.com/Oulu-IMEDS/CLIMATv2}.

Trung Dinh Quoc Dang, Huy Hoang Nguyen, Aleksei Tiulpin

Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to Vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global Vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at \url{https://github.com/Oulu-IMEDS/LoG-VMamba}.

Huy Hoang Nguyen, Cuong Nhat Nguyen, Xuan Tung Dao et al.

This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE incorporating a vision transformer (ViT-VAE). The findings reveal that ViT-VAE exhibits exemplary performance across various scenarios, whereas VAE-GRF may necessitate more intricate hyperparameter tuning to attain its optimal performance state. Additionally, to mitigate the propensity for over-reliance on results derived from the widely used MVTec dataset, this paper leverages the recently-public MiAD dataset for benchmarking. This deliberate inclusion seeks to enhance result competitiveness by alleviating the impact of domain-specific models tailored exclusively for MVTec, thereby contributing to a more robust evaluation framework. Codes is available at https://github.com/endtheme123/VAE-compare.git.

Khanh Nguyen, Huy Hoang Nguyen, Aleksei Tiulpin

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss -- an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method -- a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. The codes allowing replication of this study have been made publicly available at \url{https://github.com/Oulu-IMEDS/AdaTriplet}.

Huy Hoang Nguyen, An Vuong, Anh Nguyen et al.

Grasp detection is a fundamental robotic task critical to the success of many industrial applications. However, current language-driven models for this task often struggle with cluttered images, lengthy textual descriptions, or slow inference speed. We introduce GraspMamba, a new language-driven grasp detection method that employs hierarchical feature fusion with Mamba vision to tackle these challenges. By leveraging rich visual features of the Mamba-based backbone alongside textual information, our approach effectively enhances the fusion of multimodal features. GraspMamba represents the first Mamba-based grasp detection model to extract vision and language features at multiple scales, delivering robust performance and rapid inference time. Intensive experiments show that GraspMamba outperforms recent methods by a clear margin. We validate our approach through real-world robotic experiments, highlighting its fast inference speed.

Khanh Nguyen, Huy Hoang Nguyen, Egor Panfilov et al.

Osteoarthritis (OA) is the most common musculoskeletal disease, with knee OA (KOA) being one of the leading causes of disability and a significant economic burden. Predicting KOA progression is crucial for improving patient outcomes, optimizing healthcare resources, studying the disease, and developing new treatments. The latter application particularly requires one to understand the disease progression in order to collect the most informative data at the right time. Existing methods, however, are limited by their static nature and their focus on individual joints, leading to suboptimal predictive performance and downstream utility. Our study proposes a new method that allows to dynamically monitor patients rather than individual joints with KOA using a novel Active Sensing (AS) approach powered by Reinforcement Learning (RL). Our key idea is to directly optimize for the downstream task by training an agent that maximizes informative data collection while minimizing overall costs. Our RL-based method leverages a specially designed reward function to monitor disease progression across multiple body parts, employs multimodal deep learning, and requires no human input during testing. Extensive numerical experiments demonstrate that our approach outperforms current state-of-the-art models, paving the way for the next generation of KOA trials.

Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko et al.

In medical applications, deep learning methods are built to automate diagnostic tasks. However, a clinically relevant question that practitioners usually face, is how to predict the future trajectory of a disease (prognosis). Current methods for such a problem often require domain knowledge, and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many forecasting problem from multimodal data. Inspired by a clinical decision-making process with two agents -- a radiologist and a general practitioner, we model a prognosis prediction problem with two transformer-based components that share information between each other. The first block in this model aims to analyze the imaging data, and the second block leverages the internal representations of the first one as inputs, also fusing them with auxiliary patient data. We show the effectiveness of our method in predicting the development of structural knee osteoarthritis changes over time. Our results show that the proposed method outperforms the state-of-the-art baselines in terms of various performance metrics. In addition, we empirically show that the existence of the multi-agent transformers with depths of 2 is sufficient to achieve good performances. Our code is publicly available at \url{https://github.com/MIPT-Oulu/CLIMAT}.

Huy Hoang Nguyen, Johannes Huemer, Markus Murschitz et al.

The logistics and construction industries face persistent challenges in automating pallet handling, especially in outdoor environments with variable payloads, inconsistencies in pallet quality and dimensions, and unstructured surroundings. In this paper, we tackle automation of a critical step in pallet transport: the pallet pick-up operation. Our work is motivated by labor shortages, safety concerns, and inefficiencies in manually locating and retrieving pallets under such conditions. We present Lang2Lift, a framework that leverages foundation models for natural language-guided pallet detection and 6D pose estimation, enabling operators to specify targets through intuitive commands such as "pick up the steel beam pallet near the crane." The perception pipeline integrates Florence-2 and SAM-2 for language-grounded segmentation with FoundationPose for robust pose estimation in cluttered, multi-pallet outdoor scenes under variable lighting. The resulting poses feed into a motion planning module for fully autonomous forklift operation. We validate Lang2Lift on the ADAPT autonomous forklift platform, achieving 0.76 mIoU pallet segmentation accuracy on a real-world test dataset. Timing and error analysis demonstrate the system's robustness and confirm its feasibility for deployment in operational logistics and construction environments. Video demonstrations are available at https://eric-nguyen1402.github.io/lang2lift.github.io/