Latest AI and machine learning research in dermatology for healthcare professionals.
BACKGROUND: Diffusion-weighted imaging with higher b-value improves detection rate for prostate cancer lesions. However, obtaining high b-value DWI requires more advanced hardware and software configuration. Here we use a novel deep learning network, NAFNet, to generate a deep learning reconstructed (DLR) images from 800 b-value to mimic 1500 b-value images, and to evaluate its performance and les...
Monkeypox is a viral disease characterized by distinctive skin lesions and has been reported in many countries. The recent global outbreak has emphasized the urgent need for scalable, accessible, and accurate diagnostic solutions to support public health responses. In this study, we developed ITMAINN, an intelligent, AI-driven healthcare system specifically designed to detect Monkeypox from sk...
Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treat...
The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automa...
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncert...
Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the c...
Deep learning has transformed computer vision but relies heavily on large labeled datasets and computational resources. Transfer learning, particula...
Accurate classification of skin lesions from dermatoscopic images is essential for diagnosis and treatment of skin cancer. In this study, we investi...
Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, exist...
Advanced clinical practitioners (ACPs) play an essential role in dermatological care but often encounter challenges due to limited training in dermato...
Exploring the trustworthiness of deep learning models is crucial, especially in critical domains such as medical imaging decision support systems. C...
Artificial intelligence (AI) systems substantially improve dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further...
Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human e...
Efficient convolutional neural network (CNN) architecture designs have attracted growing research interests. However, they usually apply single rece...
BACKGROUND: Outcomes for advanced melanoma have improved following the advent of immunotherapy and targeted therapy. This heralds a need for reconside...
Multimodal medical image segmentation faces significant challenges in the context of gastric cancer lesion analysis. This clinical context is define...
Melanoma is a significant global health concern, with rising incidence rates and high mortality when diagnosed late. Artificial Intelligence (AI) mode...
BACKGROUND: Psoriasis, a chronic immune-mediated inflammatory disease (IMID), presents significant therapeutic challenges, necessitating exploration o...
Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagn...
BACKGROUND: Studies on somatic mutations in cancer typically report single-nucleotide variants in coding regions, while mutations in short tandem repe...