Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
The federated learning paradigm is wellsuited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multicenter data while protecting the privacy of participating parties. However, current research on optimization algorithms in federated learning often focuses on limited datasets and scenarios, primarily centered around natural images, with insuffi...
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we developed and evaluated a deep learning fusion model that integrates radiology reports and CT imag...
The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in sy...
The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluatio...
State Space Models (SSMs) have recently demonstrated outstanding performance in long-sequence modeling, particularly in natural language processing....
The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness ...
Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexpl...
Accurate segmentation is essential for effective treatment planning and disease monitoring. Existing medical image segmentation methods predominantl...
Advancements in AI for medical imaging offer significant potential. However, their applications are constrained by the limited availability of data ...
Although Vision Transformers (ViTs) have recently demonstrated superior performance in medical imaging problems, they face explainability issues sim...
As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown...
Medical image segmentation, particularly tumor segmentation, is a critical task in medical imaging, with U-Net being a widely adopted convolutional ...
Developing advanced medical imaging retrieval systems is challenging due to the varying definitions of `similar images' across different medical con...
Medical imaging plays a pivotal role in modern healthcare, with computed tomography pulmonary angiography (CTPA) being a critical tool for diagnosin...
Recent advances in medical vision-language models (VLMs) demonstrate impressive performance in image classification tasks, driven by their strong ze...
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require...
The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-t...
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbal...
Agentic AI systems have gained significant attention for their ability to autonomously perform complex tasks. However, their reliance on well-prepar...
Efficient evaluation of three-dimensional (3D) medical images is crucial for diagnostic and therapeutic practices in healthcare. Recent years have s...