N
Noel C. F. Codella
Researcher at IBM
Publications - 104
Citations - 7384
Noel C. F. Codella is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 30, co-authored 94 publications receiving 4748 citations. Previous affiliations of Noel C. F. Codella include Microsoft & Cornell University.
Papers
More filters
Proceedings ArticleDOI
Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Noel C. F. Codella,David A. Gutman,M. Emre Celebi,Brian Helba,Michael A. Marchetti,Stephen W. Dusza,Aadi Kalloo,Konstantinos Liopyris,Nabin K. Mishra,Harald Kittler,Allan C. Halpern +10 more
TL;DR: The most recent edition of the dermoscopic image analysis benchmark challenge as discussed by the authors was organized to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer.
Posted Content
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Noel C. F. Codella,Veronica Rotemberg,Philipp Tschandl,M. Emre Celebi,Stephen W. Dusza,David A. Gutman,Brian Helba,Aadi Kalloo,Konstantinos Liopyris,Michael A. Marchetti,Harald Kittler,Allan C. Halpern +11 more
TL;DR: This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin.
Journal ArticleDOI
Deep learning ensembles for melanoma recognition in dermoscopy images
Noel C. F. Codella,Quoc-Bao Nguyen,Sharathchandra U. Pankanti,David A. Gutman,Brian Helba,Allan C. Halpern,John R. Smith +6 more
TL;DR: A system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection is proposed.
Proceedings Article
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
TL;DR: In this paper, a deep recurrent convolutional network was proposed to learn robust representations from multi-channel EEG time-series, and demonstrated its advantages in the context of mental load classification task.
Journal ArticleDOI
Human–computer collaboration for skin cancer recognition
Philipp Tschandl,Christoph Rinner,Zoe Apalla,Giuseppe Argenziano,Noel C. F. Codella,Allan C. Halpern,Monika Janda,Aimilios Lallas,Caterina Longo,Josep Malvehy,Josep Malvehy,John Paoli,John Paoli,Susana Puig,Susana Puig,Cliff Rosendahl,H. Peter Soyer,Iris Zalaudek,Harald Kittler +18 more
TL;DR: A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human–computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.