Latest AI and machine learning research in work force for healthcare professionals.
Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this...
The growing demand for diverse and high-quality facial datasets for training and testing biometric systems is challenged by privacy regulations, data scarcity, and ethical concerns. Synthetic facial images offer a potential solution, yet existing generative models often struggle to balance realism, diversity, and identity preservation. This paper presents SCHIGAND, a novel synthetic face generatio...
Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This c...
Background Beta diversity quantifies pairwise differences between two or more communities through matrix transformations, which are either naive to ph...
Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are...
Object detection in sonar images is a key technology in underwater detection systems. Compared to natural images, sonar images contain fewer texture d...
Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired, and if ...
Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are...
There is an urgent need for triage and classification of high-volume medical imaging modalities such as computed tomography (CT), which can improve pa...
Reinforcement learning approaches for therapeutic peptide generation suffer from mode collapse, converging to narrow regions of sequence space even wh...
Vision-language pre-training (VLP) models are vulnerable to adversarial examples, particularly in black-box scenarios. Existing multimodal attacks oft...
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to al...
Recently, computer-aided diagnosis systems have been developed to support diagnosis, but their performance depends heavily on the quality and quantity...
Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We...
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole...
Medical imaging datasets often suffer from class imbalance and limited availability of pathology-rich cases, which constrains the performance of machi...
The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for...
Domain generalized semantic segmentation is an essential computer vision task, for which models only leverage source data to learn semantic segmentati...
Subject-consistent generation (SCG)-aiming to maintain a consistent subject identity across diverse scenes-remains a challenge for text-to-image (T2...
Adversarial Training (AT) is a widely adopted defense against adversarial examples. However, existing approaches typically apply a uniform training ...