Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achievi...
The application of artificial intelligence (AI) technology has realized the transformation of people's production and lifestyle, and also promoted the rapid development of the medical field. At present, the application of intelligence in the medical field is increasing. Using its advanced methods and technologies of AI, this paper aims to realize the integration of medical imaging-aided diagnosis ...
Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having...
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there...
The Society of Thoracic Radiology (STR) membership enthusiastically embraced the launch of its mentorship program, with peaks in participation and eng...
The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices...
The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative a...
The incidence of melanoma is increasing worldwide, requiring early detection to improve survival rates. Although dermoscopy is the standard non-invasi...
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensi...
Radiology Report Generation (RRG) automates the creation of radiology reports from medical imaging, enhancing the efficiency of the reporting proces...
Despite growing awareness of problems with fairness in artificial intelligence (AI) models in radiology, evaluation of algorithmic biases, or AI biase...
Artificial intelligence in medical imaging has seen unprecedented growth in the last years, due to rapid advances in deep learning and computing res...
Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses sig...
Foundation models, trained on vast amounts of data using self-supervised techniques, have emerged as a promising frontier for advancing artificial i...
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. However, adapting DL models to medical tasks remains a signific...
The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modaliti...
The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D reconstruction in radiological imaging a key res...
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised...
Recent advancements in multi-modal models have significantly improved vision-language alignment in radiology. However, existing approaches struggle ...
This paper presents MedSegFactory, a versatile medical synthesis framework that generates high-quality paired medical images and segmentation masks ...