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Few-shot object detection (FSOD) recently garnered increasing attention in remote sensing images (RSIs). Although FSOD has achieved success in natural sense, the large size and complex backgrounds of RSIs make FSOD espec...

Low-resolution images often lack fine detail and are prone to blurring, noise, and distortions, making accurate assessment challenging. This issue becomes even more challenging for applications, such as land mapping, env...

Abstract This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artificial Intelligence and Machine Learning (AI/ML) technologies f...

Abstract Timely and accurate detection of COVID-19 is essential for rapid intervention and preventing the spread of the disease, especially among vulnerable populations. This study introduces a new two-stage methodology ...

Radiography or X-ray imaging is a diagnostic imaging used in medicine to produce images of inner anatomical structures such as bones, teeth, lungs, and chest organs. Impulse noise is one of the major factors that degrade...

Abstract Virtual staining technology offers a promising solution to overcome the time-consuming and sample-consumption nature of conventional histochemical staining in breast cancer pathology. This study presents a novel...

Abstract Quantitative analysis of fluorescence microscopy images remains a significant challenge in cytopathological assessments, as subjective interpretation and inter‐observer variability hinder reproducibility and s...

Abstract Contrast enhancement technique is a prevalent problem in aiding interpretation of Magnetic Resonance Imaging (MRI) images. A low contrast MRI image may prove to be problematic in identifying medical problems suc...

With the rapid advancement of remote sensing technology, target detection in remote sensing imagery plays a crucial role in military reconnaissance, urban planning, and other fields. However, there are problems such as l...

Abstract Background Leishmaniasis is a vector-borne parasitic disease caused by Leishmania protozoa. The disease manifests in several clinical presentations including cutaneous, mucocutaneous, and visceral leishmaniasis....

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infectio...

Abstract Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable...

Estimating three-dimensional morphological traits such as volume from two-dimensional RGB images presents inherent challenges due to the loss of depth information, projection distortions, and occlusions under field condi...

Background and purpose: Volumetric Interpolated Breath-hold Examination (VIBE) magnetic resonance imaging (MRI) can depict flap applicators of High-Dose-Rate (HDR) surface brachytherapy. Evaluating criteria for catheter ...

The evolution of starspots of the giant primaries of RS CVn systems is typically detected indirectly with photometric and spectroscopic monitoring. These observations suggest slowly-evolving stellar surfaces and can cons...

To address the challenge of extracting multi-scale features from distribution line insulators in complex inspection environments—where varying scales and background interference complicate detection—this study propos...

Abstract Objective To investigate whether virtual unenhanced (VUE) images generated from spectral CT using the rapid kVp switching technique can serve as a substitute for true unenhanced (TUE) images in the assessment of...

Abstract The development of trustworthy AI models is crucial, particularly for critical medical applications such as brain tumor detection using MRI images. However, medical images are being increasingly targeted by adve...

Abstract Neoadjuvant chemotherapy (NAC) induces critical morphological and vascular changes in breast cancer tumors, yet existing deep learning techniques in medical imaging tend to neglect these longitudinal changes thr...

Abstract Objective To evaluate the diagnostic value of Revolution APEX energy spectrum CT imaging and quantitative parameters in T staging of pancreatic cancer (PCa). Methods Energy spectrum CT of 105 patients diagnosed ...

Abstract Objectives This dataset was specifically collected to support the development and evaluation of computer-aided diagnosis (CAD) systems for breast cancer detection. The dataset, Ma’amon Diagnostic Center Mammog...

Abstract Objectives To develop and evaluate a deep learning model for diagnosing untreated rheumatoid arthritis (RA) using digital camera images of bilateral dorsal hands, benchmarking its performance against the widely-...

Abstract Deep learning-based image semantic segmentation techniques have made great strides in recent years. However, they still need large amounts of finely annotated image data, and generalizing the model from known cl...

To achieve high accuracies in deep learning-based inspection of IC markings, a large number of manually labeled images of good and defective markings are needed. In many IC production lines, only a handful of labeled ima...