Latest AI and machine learning research in dermatology for healthcare professionals.
Vision-language models in pathology enable multimodal case retrieval and automated report generation. Many of the models developed so far, however, have been trained on pathology reports that include information which cannot be inferred from paired whole slide images (e.g., patient history), potentially leading to hallucinated sentences in generated reports. To this end, we investigate how the s...
Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study presents a novel exploration, being the first to systematically evaluate segmentation, registration, and classification tasks across multiple imaging modalities. Integrating both classical and deep learning (DL) approaches in addressing brain MRI tissu...
BACKGROUND: In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from expe...
We can achieve fast and consistent early skin cancer detection with recent developments in computer vision and deep learning techniques. However, th...
Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...
Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have di...
Recent research in medical image analysis with deep learning almost exclusively focuses on grid- or voxel-based data representations. We challenge t...
BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung can...
BACKGROUND: Skin cancer is the most common cancer worldwide. Early diagnosis is crucial to improving patient survival and morbidity. Artificial intell...
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. Thi...
The hemorrhagic lesion segmentation plays a critical role in ophthalmic diagnosis, directly influencing early disease detection, treatment planning,...
Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and...
Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Di...
This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of ...
Artificial intelligence-assisted imaging analysis has made substantial strides in tumor diagnosis and management. Here we present PASTA, a pan-tumor...
Medical images often exhibit low and blurred contrast between lesions and surrounding tissues, with considerable variation in lesion edges and shape...
Atherosclerosis is a chronic inflammatory disease of the artery wall. The early stages of atherosclerosis are driven by interactions between lipids ...
Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of complex...
Deep Learning approaches in dermatological image classification have shown promising results, yet the field faces significant methodological challen...
Focal deficits in ischaemic stroke result from impaired perfusion downstream of a critical vascular occlusion. While parenchymal lesions are traditi...