TY - GEN
T1 - Generating highly realistic images of skin lesions with GANs
AU - Baur, Christoph
AU - Albarqouni, Shadi
AU - Navab, Nassir
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.
AB - As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.
UR - http://www.scopus.com/inward/record.url?scp=85054877487&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01201-4_28
DO - 10.1007/978-3-030-01201-4_28
M3 - Conference contribution
AN - SCOPUS:85054877487
SN - 9783030012007
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 260
EP - 267
BT - OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis - 1st International Workshop, OR 2.0 2018 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, 3rd International Workshop, ISIC 2018 Held in Conjunction with MICCAI 2018
A2 - Malpani, Anand
A2 - Zenati, Marco A.
A2 - Oyarzun Laura, Cristina
A2 - Celebi, M. Emre
A2 - Sarikaya, Duygu
A2 - Codella, Noel C.
A2 - Halpern, Allan
A2 - Erdt, Marius
A2 - Maier-Hein, Lena
A2 - Xiongbiao, Luo
A2 - Wesarg, Stefan
A2 - Stoyanov, Danail
A2 - Taylor, Zeike
A2 - Drechsler, Klaus
A2 - Dana, Kristin
A2 - Martel, Anne
A2 - Shekhar, Raj
A2 - De Ribaupierre, Sandrine
A2 - Reichl, Tobias
A2 - McLeod, Jonathan
A2 - González Ballester, Miguel Angel
A2 - Collins, Toby
A2 - Linguraru, Marius George
PB - Springer Verlag
T2 - 1st International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and 1st International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -