Melanoma is the most lethal type of skin cancer. Patients are vulnerable to mental health illnesses which can reduce the effectiveness of the cancer treatment and the patients adherence to drug plans. It is crucial to preserve the mental health of patients while they are receiving treatment. However, current art therapy approaches are not personal and unique to the patient. We aim to provide a well-trained image style transfer model that can quickly generate unique art from personal dermoscopic melanoma images as an additional tool for art therapy in disease management of melanoma. Visual art appreciation as a common form of art therapy in disease management that measurably reduces the degree of psychological distress. We developed a network based on the cycle-consistent generative adversarial network for style transfer that generates personalized and unique artworks from dermoscopic melanoma images. We developed a model that converts melanoma images into unique flower-themed artworks that relate to the shape of the lesion and are therefore personal to the patient. Further, we altered the initial framework and made comparisons and evaluations of the results. With this, we increased the options in the toolbox for art therapy in disease management of melanoma. The development of an easy-to-use user interface ensures the availability of the approach to stakeholders. The transformation of melanoma into flower-themed artworks is achieved by the proposed model and the graphical user interface. This contribution opens a new field of GANs in art therapy and could lead to more personalized disease management.

翻译：黑色素瘤是致死率最高的皮肤癌类型。患者易患心理健康疾病，这会降低癌症治疗效果及患者对药物方案的依从性。在患者接受治疗期间，维护其心理健康至关重要。然而，当前的艺术疗法方法缺乏针对患者的个性化和独特性。本研究旨在提供经过良好训练的影像风格迁移模型，该模型能够从患者个人皮肤镜黑色素瘤图像中快速生成独特艺术作品，作为黑色素瘤疾病管理中艺术疗法的辅助工具。视觉艺术欣赏作为疾病管理中常见的艺术疗法形式，可显著降低心理困扰程度。我们基于循环一致生成对抗网络开发了风格迁移模型，能从皮肤镜黑色素瘤图像生成个性化、独特的艺术作品。该模型可将黑色素瘤图像转换为与病灶形态相关的花卉主题艺术作品，从而为患者提供个性化体验。进一步地，我们改进了初始框架并对结果进行了比较与评估，从而丰富了黑色素瘤疾病管理艺术疗法的工具库。通过开发易用的用户界面，确保了相关利益方对该方法的可及性。所提出的模型与图形用户界面成功实现了黑色素瘤图像向花卉主题艺术作品的转化。这项研究开辟了生成对抗网络在艺术疗法中的新领域，有望推动更个性化的疾病管理。