Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00384-006-0119-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
[Not Available].
Surg Technol Int
January 2025
Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, New York.
Thermal or burn injuries cause coagulative necrosis of the epidermis and underlying tissues and the resultant wounds can be long lasting and highly painful. Depending on the depth of a burn, management ranges from local wound care to surgical intervention. When presented with deep-partial thickness and full-thickness burns, autologous skin grafting has been the mainstay of management to prevent scarring and promote healing.
View Article and Find Full Text PDFQuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.
Anal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
View Article and Find Full Text PDFPersonalized Photoprotection: Expert Consensus and Recommendations From a Delphi Study Among Dermatologists.
Photodermatol Photoimmunol Photomed
January 2025
Department of Medicine and Medical Specialties, University of Alcalá de Henares, Madrid, Spain.
Background: Recommending comprehensive personalized photoprotection requires an accurate assessment of the patient's skin, including phototype, lifestyle, exposure conditions, environmental factors, and concomitant cutaneous conditions as well as deep knowledge of the available options: sunscreen ingredients (type of filters, spectrum coverage, sun protection factor, enhanced active ingredients), oral photoprotection, and other methods of sun protection and avoidance.
Objectives: To establish consensus-based recommendations endorsed by an international panel of experts for personalized medical photoprotection recommendations that are applicable globally.
Methods: A two-round Delphi study was designed to determine the degree of agreement and relevance of aspects related to personalized medical photoprotection.
Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.
Sci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFLiteMamba-Bound: A Lightweight Mamba-based Model with Boundary-Aware and Normalized Active Contour Loss for Skin Lesion Segmentation.
Methods
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam. Electronic address:
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!