Latest AI and machine learning research in work force for healthcare professionals.
Deep learning-based weed control systems often suffer from limited training data diversity and constrained on-board computation, impacting their real-world performance. To overcome these challenges, we propose a framework that leverages Stable Diffusion-based inpainting to augment training data progressively in 10% increments -- up to an additional 200%, thus enhancing both the volume and divers...
We introduce a novel question-answering (QA) dataset using echocardiogram reports sourced from the Medical Information Mart for Intensive Care database. This dataset is specifically designed to enhance QA systems in cardiology, consisting of 771,244 QA pairs addressing a wide array of cardiac abnormalities and their severity. We compare large language models (LLMs), including open-source and bio...
Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos....
Image captioning tasks usually use two-stage training to complete model optimization. The first stage uses cross-entropy as the loss function for op...
Bayesian Neural Networks (BNNs) provide a promising framework for modeling predictive uncertainty and enhancing out-of-distribution robustness (OOD)...
Medical education faces challenges in scalability, accessibility, and consistency, particularly in clinical skills training for physician-patient co...
Pain is a complex, multidimensional experience involving significant challenges in both diagnosis and management. While acute pain serves as a critica...
The advent of Large Language Models (LLMs) offers potential solutions to address problems such as shortage of medical resources and low diagnostic c...
Tuberculosis (TB) is a infectious global health challenge. Chest X-rays are a standard method for TB screening, yet many countries face a critical s...
Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and i...
Face recognition (FR) stands as one of the most crucial applications in computer vision. The accuracy of FR models has significantly improved in rec...
STEM fields are traditionally male-dominated, with gender biases shaping perceptions of job accessibility. This study analyzed gender representation...
In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabil...
Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural ne...
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensi...
This study addresses the challenge of reconstructing unseen ECG signals from PPG signals, a critical task for non-invasive cardiac monitoring. While...
Linker generation is critical in drug discovery applications such as lead optimization and PROTAC design, where molecular fragments are assembled in...
Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection...
Objective: To optimize in-context learning in biomedical natural language processing by improving example selection. Methods: We introduce a novel m...
Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex en...