Latest AI and machine learning research in universal precautions for healthcare professionals.
Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, where high-resolution data is often unavailable due to computational constraints. To address this challenge, we propose MaskAttn-UNet, a novel segmentation framework that enhances the traditional U-Net architecture via a mask attention mechanism. Our m...
Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. However, existing methods often rely on user-provided masks, introducing complexity and performance degradation due to imperfect inputs, as shown in Fig.1(a). To address this, we propose a Mask-Free VITON (MF-VITON) fram...
While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practica...
Deep learning has significantly advanced PET image re-construction, achieving remarkable improvements in image quality through direct training on si...
Despite recent advancements in federated learning (FL), the integration of generative models into FL has been limited due to challenges such as high...
Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Alth...
Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segmentation tasks. However, when applying it to $360^...
Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depe...
Resistive tactile sensing gloves have captured the interest of researchers spanning diverse domains, such as robotics, healthcare, and human-compute...
Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in variou...
Recently, 3D Gaussian Splatting (3D-GS) has emerged, showing real-time rendering speeds and high-quality results in static scenes. Although 3D-GS sh...
Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged obj...
Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as the data labeling process or the sampling of trainin...
The Segment Anything Model (SAM) has revolutionized open-set interactive image segmentation, inspiring numerous adapters for the medical domain. How...
Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, s...
The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution...
Autonomous navigation is a fundamental task for robot vacuum cleaners in indoor environments. Since their core function is to clean entire areas, ro...
BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide...
Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrho...
Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patie...