2025 February - 39 articles
This study explores a method of detecting smoke plumes effectively as the early sign of a forest fire. Convolutional neural networks (CNNs) have been widely used for forest fire detection; however, they have not been customized or optimized for smoke...
Diffusion models are among the most common techniques used for image generation, having achieved state-of-the-art performance by implementing auto-regressive algorithms. However, multi-step inference processes are typically slow and require extensive...
This study examines how different filming techniques can enhance the quality of 3D reconstructions with a particular focus on their use in indoor crime scene investigations. Using Neural Radiance Fields (NeRF) and Gaussian Splatting, we explored how...
Timely prediction of collisions enables advanced driver assistance systems to issue warnings and initiate emergency maneuvers as needed to avoid collisions. With recent developments in computer vision and deep learning, collision warning systems that...
FDG PET/CT provides complementary metabolic information with greater sensitivity and specificity than conventional imaging modalities for evaluating local recurrence, nodal, and distant metastases in patients with cervical cancer. PET/CT can also be...
The mechanism of post-acute sequelae of SARS-CoV-2 (PASC) is unknown. Using optical coherence tomography angiography (OCT-A), we compared retinal foveal avascular zone (FAZ), vessel density (VD), and vessel length density (VLD) in non-hospitalized Ne...
Prostate cancer, a prevalent malignancy affecting males globally, underscores the critical need for precise prostate segmentation in diagnostic imaging. However, accurate delineation via MRI still faces several challenges: (1) The distinction of the...
Individual differences in attachment orientations may influence how we process emotionally significant stimuli. As one of the most important sources of emotional information are facial expressions, we examined whether there is an association between...
Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation a...
This systematic literature review aims to understand new developments and challenges in facial recognition technology. This will provide an understanding of the system principles, performance metrics, and applications of facial recognition technology...
Trabecular bone architecture has important implications for the mechanical strength of bone. Trabecular elements appear as signal void when imaged utilizing conventional magnetic resonance imaging (MRI) sequences. Ultrashort echo time (UTE) MRI can a...
Segmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these networks face significant challenges, including the need for extensive annotated patient data, ti...
Renal denervation to treat arterial hypertension is growing in adoption but still shows inconsistent results. Device improvement is difficult, as there is currently no way to study the immediate success of renal denervation devices in living tissue....
Transformer-based image registration methods have achieved notable success, but they still face challenges, such as difficulties in representing both global and local features, the inability of standard convolution operations to focus on key regions,...
This study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with I...
Zero-shot counting is a subcategory of Generic Visual Object Counting, which aims to count objects from an arbitrary class in a given image. While few-shot counting relies on delivering exemplars to the model to count similar class objects, zero-shot...
Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder–decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian no...
The accurate segmentation of lung regions in computed tomography (CT) scans is critical for the automated analysis of lung diseases such as chronic obstructive pulmonary disease (COPD) and COVID-19. This paper focuses on enhancing the accuracy of U-N...
The increasing reliance on 3D scanning and meshless methods highlights the need for algorithms optimized for point-cloud geometry representations in CAE simulations. While voxel-based binning methods are simple, they often compromise geometry and top...
The optimal timing for treatment of patients with isolated asymptomatic severe aortic stenosis and preserved left ventricular ejection fraction is still controversial and research is ongoing. Once a diagnosis has been performed and other cardiac como...
Differentiating atypical lipomatous tumors (ALTs) from lipomas using imaging techniques is a challenge, and the biopsy with immunohistochemical determination of murine double minute 2 (MDM2) oncogene is the gold standard. We are looking for a managem...
The rapid development of machine learning and artificial intelligence approaches is finding ever wider application in various areas of life. This paper considers the problem of improving editorial and publishing processes, namely self-publishing, whe...
The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist for this decision. Here, lung ultrasound (LU) data, blood gas analysis (BGA), and clinical parameters from ve...
Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand–eye calibration is required to k...
Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time...
In this paper, we address the challenge of fingerprint liveness detection by proposing a dual pre-trained model approach that combines VGG16 and ResNet50 architectures. While existing methods often rely on a single feature extraction model, they may...
This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. While this is an exploratory study, the ultimate aim is to address the scarcity of data in dental research and...
Visual clarity is crucial for shoulder arthroscopy, directly influencing surgical precision and outcomes. Despite advances in imaging technology, intraoperative bleeding remains a significant obstacle to optimal visibility, with subjective evaluation...
As 3D printing technology expands rapidly in medical disciplines, the accuracy evaluation of 3D-printed medical models is required. However, no established guidelines to assess the dimensional error of anatomical models exist. This study aims to eval...
The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitat...
Breast cancer, as of 2022, is the most prevalent type of cancer in women. Breast density—a measure of the non-fatty tissue in the breast—is a strong risk factor for breast cancer that can be estimated from mammograms. The importance of st...
The transcription of historical manuscripts aims at making our cultural heritage more accessible to experts and also to the larger public, but it is a challenging and time-intensive task. This paper contributes an automated solution for text layout r...
Social biases in generative models have gained increasing attention. This paper proposes an automatic evaluation protocol for text-to-image generation, examining how gender bias originates and perpetuates in the generation process of Stable Diffusion...
Object segmentation algorithms have heavily relied on deep learning techniques to estimate the count of grapes which is a strong indicator for the yield success of grapes. The issue with using object segmentation algorithms for grape analytics is tha...
Defect detection methods have greatly assisted human operators in various fields, from textiles to surfaces and mechanical components, by facilitating decision-making processes and reducing visual fatigue. This area of research is widely recognized a...
This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various op...
The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, divers...
This study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels, using ord...
Remote sensing images have the characteristics of high complexity, being easily distorted, and having large-scale variations. Moreover, the motion of remote sensing targets usually has nonlinear features, and existing target tracking methods based on...
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