Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets, consuming significant computational time, energy, and resources. There is a need for more efficient methods that can achieve comparable or superior diagnostic performance without the associated resource burden. We investigated the feasi...
Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which limits the applicability to current FSMIS tasks. To overcome this limitation, we focus on the cross-domain few-shot medical image segmentation (CD-FSMIS) task, aiming to develop a generalized model capable of adapting to ...
The field of medical imaging AI is currently undergoing rapid transformations, with methodical research increasingly translated into clinical practi...
In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent ...
Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on mill...
Deep learning has seen remarkable advancements in machine learning, yet it often demands extensive annotated data. Tasks like 3D semantic segmentati...
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardiz...
Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data f...
Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic di...
Medical image analysis is crucial in modern radiological diagnostics, especially given the exponential growth in medical imaging data. The demand fo...
Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to e...
Biomedical data is inherently multimodal, consisting of electronic health records, medical imaging, digital pathology, genome sequencing, wearable s...
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This sy...
Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in fr...
Data scarcity is a major limiting factor for applying modern machine learning techniques to clinical tasks. Although sufficient data exists for some...
In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streaml...
Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supe...
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several ben...
Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders i...
Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinica...