A5006729270- Cutaneous Melanoma Detection and Management
- Nonmelanoma Skin Cancer Studies
- Cutaneous lymphoproliferative disorders research
- AI in cancer detection
- Cancer and Skin Lesions
- Optical Coherence Tomography Applications
- Infectious Diseases and Mycology
- Genetic and rare skin diseases.
- Cell Image Analysis Techniques
- Melanoma and MAPK Pathways
- Skin Protection and Aging
- Nail Diseases and Treatments
- Digital Imaging in Medicine
- melanin and skin pigmentation
- Vascular Tumors and Angiosarcomas
- Ear and Head Tumors
- Tumors and Oncological Cases
- Dermatologic Treatments and Research
- Sarcoma Diagnosis and Treatment
- Autoimmune Bullous Skin Diseases
- CAR-T cell therapy research
- Cancer Diagnosis and Treatment
- Telemedicine and Telehealth Implementation
- Dupuytren's Contracture and Treatments
- Autoimmune and Inflammatory Disorders
Andreas Sygros Hospital
2018-2025
National and Kapodistrian University of Athens
2018-2025
Memorial Sloan Kettering Cancer Center
2016-2024
Pontificia Universidad Católica de Chile
2019-2023
Advanced Dermatology
2022
Microsoft (United States)
2022
Kitware (United States)
2022
Medical University of Vienna
2022
Emory University
2022
Melanoma Research Alliance
2022
This article describes the design, implementation, and results of latest installment dermoscopic image analysis benchmark challenge. The goal is to support research development algorithms for automated diagnosis melanoma, most lethal skin cancer. challenge was divided into 3 tasks: lesion segmentation, feature detection, disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), approximately 50 attendees,...
This work summarizes the results of largest skin image analysis challenge in world, hosted by International Skin Imaging Collaboration (ISIC), a global partnership that has organized world's public repository dermoscopic images skin. The was 2018 at Medical Image Computing and Computer Assisted Intervention (MICCAI) conference Granada, Spain. dataset included over 12,500 across 3 tasks. 900 users registered for data download, 115 submitted to lesion segmentation task, 25 attribute detection...
BackgroundMultiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting cornerstone a successful translation AI-based tools into clinicopathological practice.ObjectiveThe objective study was systematically analyse current state research on reader involving melanoma and assess their potential clinical relevance by evaluating three main aspects: test set characteristics...
The use of artificial intelligence (AI) is accelerating in all aspects medicine and has the potential to transform clinical care dermatology workflows. However, develop image-based algorithms for applications, comprehensive criteria establishing development performance evaluation standards are required ensure product fairness, reliability, safety.
The use of artificial intelligence (AI) algorithms for the diagnosis skin diseases has shown promise in experimental settings but not been yet tested real-life conditions.To assess diagnostic performance and potential clinical utility a 174-multiclass AI algorithm telemedicine setting.Prospective, accuracy study including consecutive patients who submitted images teledermatology evaluation. treating dermatologist chose single image to upload web application during teleconsultation. A...
Abstract AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public datasets are comprised of dermoscopic photos and limited by selection bias, lack standardization, lend themselves development that can only be used skilled clinicians. The SLICE-3D (“Skin Lesion Image Crops Extracted from 3D TBP”) dataset described here addresses those concerns contains images over 400,000 distinct lesions seven dermatologic centers around the world....
Basal cell carcinoma (BCC) is the most common skin cancer. Dermoscopic imaging has improved diagnostic accuracy; however, diagnosis of nonpigmented BCC remains limited to arborizing vessels, ulceration, and shiny white structures.To assess multiple aggregated yellow-white (MAY) globules as a feature for BCC.In this retrospective, single-center, case-control study, tumors, determined clinically, were identified from database lesions consecutively biopsied during 7-year period (January 1,...
Background: The group of histopathologically aggressive BCC subtypes includes morpheaform, micronodular, infiltrative and metatypical BCC. Since these tumors are at increased risk recurring, micrographically controlled surgery is considered the best therapeutic option. Although dermoscopy significantly improves clinical recognition BCC, scarce evidence exists on their dermoscopic criteria. Aim: To investigate characteristics subtypes. Materials Methods: Dermoscopic images were analyzed for...
Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts identification dermoscopic structures known to be relatively poor. Expert labeling medical data a bottleneck in development machine learning (ML) tools, and crowdsourcing has been demonstrated as cost- time-efficient method annotation images.The aim this study demonstrate that can label basic from images lesions with similar reliability group experts.First, we obtained labels 248 melanocytic 31...