Generative Modeling for Melanoma Detection
Examensarbete för masterexamen
Complex adaptive systems (MPCAS), MSc
Zoubi, Mohamad Khir
Early detection significantly reduces deaths associated with melanoma, a skin cancer. Despite this information, 80% of skin cancer related deaths are attributed to malignant melanoma. Melanomas are difficult to diagnose by a dermatologist (skin doctor) therefore many patients undergo unnecessary surgeries to get a biopsy that can confirm the disease. Minimizing unnecessary surgeries would leave more resources that in turn could lead to a higher frequency of earlier diagnosed melanomas. Machine learning algorithms have shown a great potential in the field of medicine and could be deployed to help doctors diagnose melanomas. To obtain a high performing model it is crucial to have a large and balanced dataset. The scarcity of labeled publicly available medical images makes applying machine learning an obstacle in this field, thus hindering development. A solution to this problem could be to synthesize realistic looking images using deep neural networks. One such network is Generative Adversarial Network (GAN), which has been shown successful in producing images in the field of medicine. This thesis explores the generation of synthetic image data for medical purposes and how such data can be evaluated. We utilize StyleGAN2-ADA to generate synthetic images of melanoma lesions that we evaluate using both qualitative and quantitative measures. A survey was made to establish if experts can identify generated images in a mixed dataset. The expert dermatologists found the images difficult to distinguish from real ones, accordingly proving that we can synthesize realistically looking images of melanomas. Using a classifier trained on synthetic melanoma and non-melanoma images we are also able to reach a high accuracy when validating against real data. Our results show that synthetic images are verifiably realistic looking. From our research we are able to conclude that synthetic data can be the answer to further development of classification algorithms in a clinical setting.