Dermoscopy of early-stage melanoma can be challenging, and repeated examination at three-month intervals may disclose subtle changes. In patients with atypical nevus syndrome or more than 50 nevi, repetitive excision of benign lesions does not guarantee that melanomas will be identified at an early stage and exposes patients to potentially disfiguring surgery. We present the case of a high-risk patient where repeated dermoscopy of an in situ melanoma showed that part of the pigment network had coarsened, even though the lesion had not changed macroscopically.
Download full-text PDF |
Source |
---|
J Eur Acad Dermatol Venereol
December 2024
School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Colloids Surf B Biointerfaces
February 2025
National Engineering Research Center for Biomaterials, Sichuan University, Chengdu, Sichuan 610064, China; College of Biomedical Engineering, Sichuan University, Chengdu 610064, China.
Sensors (Basel)
August 2024
School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remain in accurately delineating the boundaries of lesion regions in dermoscopic images with irregular shapes, blurry edges, and occlusions by artifacts. To address these issues, a multi-attention codec network with selective and dynamic fusion (MASDF-Net) is proposed for skin lesion segmentation in this study.
View Article and Find Full Text PDFExp Dermatol
August 2024
Department of Dermatology, Copenhagen University Hospital, Copenhagen, Denmark.
Int J Comput Assist Radiol Surg
August 2024
Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany.
Purpose: The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy of image segmentation is still impacted by various interference factors encountered during image acquisition.
Methods: To address this challenge, this paper proposes a loss function designed to mine specific pixel information which dynamically changes during training process.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!