Latest AI and machine learning research in dermatology for healthcare professionals.
Background: Precision oncology relies on accurate interpretation of tumour-detected gene variants, to guide personalized treatment decisions. However, accurate interpretation of variants in context requires extensive information that is often buried within unstructured biomedical literature and obscured by inconsistent nomenclature, making manual retrieval labour-intensive and prone to omissions. ...
Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WM...
Basal cell carcinoma (BCC) care follows a sequence of decisions from triage to pathological subtyping and depth assessment, and the information availa...
White-light imaging (WLI) and narrow-band imaging (NBI) provide complementary views of endoscopic lesions, but their paired observations are often spa...
Acne vulgaris affects most adolescents and many adults. Accurate severity grading guides treatment, monitoring, and clinical trial endpoints, but manu...
Background/Objectives: Dermoscopic skin lesion classifiers often lose accuracy under domain shift across imaging devices, illumination, and capture ar...
Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional les...
When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show ...
Background Histological diagnosis of early-stage mycosis fungoides (MF) is hindered by profound overlap with benign inflammatory dermatoses (BIDs), le...
Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts...
In clinical oncology studies, metastatic cancer is commonly evaluated using "Response Evaluation Criteria in Solid Tumors" (RECIST), in which the diam...
Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions a...
Accurate grading of cervical biopsies on Hematoxylin and Eosin (H&E) stained whole slide images (WSIs) is essential for distinguishing high grade lesi...
Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet rem...
Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical ima...
Diabetic retinopathy is a leading cause of preventable blindness; its early lesions are small, low contrast, and easily missed in manual screening. Mo...
Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical researc...
Accurate skin lesion classification can benefit from lesion segmentation masks, but requiring masks or an auxiliary segmentation model during inferenc...
Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative feature...
Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information ...