A5035102732- Cutaneous Melanoma Detection and Management
- Nonmelanoma Skin Cancer Studies
- AI in cancer detection
- Cutaneous lymphoproliferative disorders research
- Cell Image Analysis Techniques
- Nail Diseases and Treatments
- Optical Coherence Tomography Applications
- Melanoma and MAPK Pathways
- Digital Imaging in Medicine
- Cancer and Skin Lesions
- Genetic and rare skin diseases.
- Skin Protection and Aging
- Medicine and Dermatology Studies History
- Tumors and Oncological Cases
- Allergic Rhinitis and Sensitization
- Infectious Diseases and Mycology
- Virus-based gene therapy research
- CAR-T cell therapy research
- Dermatological diseases and infestations
- Diagnosis and Treatment of Venous Diseases
- Remote Sensing and LiDAR Applications
- melanin and skin pigmentation
- Autoimmune Bullous Skin Diseases
- Artificial Intelligence in Healthcare and Education
- Eosinophilic Disorders and Syndromes
Medical University of Vienna
2016-2025
Universitätszahnklinik Wien
2017-2023
Memorial Sloan Kettering Cancer Center
2022
Microsoft (United States)
2022
Kitware (United States)
2022
Emory University
2022
National and Kapodistrian University of Athens
2022
Melanoma Research Alliance
2022
Advanced Dermatology
2022
University Dermatology
2020
Training of neural networks for automated diagnosis pigmented skin lesions is hampered by the small size and lack diversity available datasets dermatoscopic images. We tackle this problem releasing HAM10000 ("Human Against Machine with 10000 training images") dataset. collected images from different populations acquired stored modalities. Given we had to apply acquisition cleaning methods developed semi-automatic workflows utilizing specifically trained networks. The final dataset consists...
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...
Abstract Prior skin image datasets have not addressed patient-level information obtained from multiple lesions the same patient. Though artificial intelligence classification algorithms achieved expert-level performance in controlled studies examining single images, practice dermatologists base their judgment holistically on The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice,...
Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, most common types skin cancer are nonpigmented and nonmelanocytic, more difficult to diagnose.To compare a CNN-based classifier with that physicians different levels experience.A classification model was trained on 7895 dermoscopic 5829 close-up images lesions excised at primary clinic between January 1, 2008, July 13, 2017, for combined evaluation both imaging...
Abstract While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies generally considered only a single clinical/macroscopic image and output binary decision. In this work, we presented method which combines multiple imaging modalities together with patient metadata to improve the performance of automated diagnosis. We evaluated our on classification task comparison as well five class representative real‐world clinical scenario....
Highlights•A market-approved convolutional neural network (CNN) trained on dermoscopic images was tested against 96 dermatologists.•Test data included a broad range of skin lesions and compiled from external sources not involved in CNN training.•Dermatologists indicated their management decisions after reviewing clinical, dermoscopic, textual case information.•In this setting dermatologists performed par with the CNN's classifications based alone.AbstractBackgroundConvolutional networks...
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.
We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. utilized nonuniform rewards and penalties based on expert-generated tables, balancing benefits harms of various errors, which were applied reinforcement learning. Compared with supervised learning, learning model improved sensitivity for melanoma from 61.4% 79.5% (95% confidence interval (CI): 73.5-85.6%) basal cell...
OBJECTIVES To characterize dermoscopic criteria of squamous cell carcinoma (SCC) and keratoacanthoma to compare them with other lesions. DESIGN Observer-masked study consecutive lesions performed from March 1 through December 31, 2011. SETTING Primary care skin cancer practice in Brisbane, Australia. PARTICIPANTS A total 186 patients 206 MAIN OUTCOME MEASURES Sensitivity, specificity, predictive values, odds ratios. RESULTS In a retrospective analysis 60 invasive SCC 43 cases, keratin,...
The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become standard for melanoma.To critically review current literature and compare diagnostic accuracy with human experts.The MEDLINE, arXiv, PubMed Central databases were searched to identify eligible studies published between January 1, 2002, December 31, 2018.Studies reported on automated systems melanoma selected. Search terms included melanoma, diagnosis, detection, computer...
Journal Article The BRAAFF checklist: a new dermoscopic algorithm for diagnosing acral melanoma Get access A. Lallas, Lallas Skin Cancer Unit Arcispedale Santa Maria Nuova IRCCS Viale Risorgimento 80 42100 Reggio Emilia Italy Correspondence Aimilios Lallas. E‐mail: emlallas@gmail.com Search other works by this author on: Oxford Academic Google Scholar Kyrgidis, Kyrgidis H. Koga, Koga Department of Dermatology Shinshu University School Medicine Matsumoto Japan E. Moscarella, Moscarella P....
The diagnosis of flat pigmented lesions on the face is challenging because morphologic overlap biologically different and unknown significance dermatoscopic patterns.To better characterize patterns various types facial to analyse their by calculating relative risks diagnostic values.We prospectively analysed images 240 skin collected consecutively from 195 patients (41.5% females, mean age: 61 ± 14 years) between 2007 March 2012 in a primary cancer practice situated Queensland,...