COVID-19

E-SAM: Training-Free Segment Every Entity Model

Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited to open-world applications with adaptation to diverse and dynamically changing environments, where new and previously unseen entities may appear frequently. Existing ES methods either require large annotated datasets ...

Deep Learning-Based Direct Leaf Area Estimation using Two RGBD Datasets for Model Development

Estimation of a single leaf area can be a measure of crop growth and a phenotypic trait to breed new varieties. It has also been used to measure leaf area index and total leaf area. Some studies have used hand-held cameras, image processing 3D reconstruction and unsupervised learning-based methods to estimate the leaf area in plant images. Deep learning works well for object detection and segmen...

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework

Data-driven AI is establishing itself at the center of evidence-based medicine. However, reports of shortcomings and unexpected behavior are growing...

Using Causal Inference to Explore Government Policy Impact on Computer Usage

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown...

SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAM

Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, this trend has extended to medical imaging. Yet, o...

Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching

Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality da...

FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model

Solving medical imaging data scarcity through semantic image generation has attracted significant attention in recent years. However, existing metho...

Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms

Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. In this study, deep-learning model...

MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation

Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, wh...

MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input

Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-...

SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories

While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practica...

3D Medical Imaging Segmentation on Non-Contrast CT

This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-...

Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework

Deep learning has significantly advanced PET image re-construction, achieving remarkable improvements in image quality through direct training on si...

PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models

Despite recent advancements in federated learning (FL), the integration of generative models into FL has been limited due to challenges such as high...

Accurate INT8 Training Through Dynamic Block-Level Fallback

Transformer models have achieved remarkable success across various AI applications but face significant training costs. Low-bit training, such as IN...

Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition

Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission ...

Customized SAM 2 for Referring Remote Sensing Image Segmentation

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Alth...

OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation

Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segmentation tasks. However, when applying it to $360^...

Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images

Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have ...

DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion

Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depe...