Generative Adversarial Networks based Skin Lesion Segmentation

Skin cancer is a serious condition that requires accurate identification and treatment. One way to assist clinicians in this task is by using computer-aided diagnosis (CAD) tools that can automatically segment skin lesions from dermoscopic images. To this end, a new adversarial learning-based framework called EGAN has been developed. This framework uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path and an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. Additionally, a morphology-based smoothing loss is implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018 and outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. This represents a 2% increase in Dice Coefficient, 1% increase in Jaccard Index, and 1% increase in Accuracy

A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images

[EN] The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions’ shape should be integrated as a fundamental part of future detection techniques in clinical screening.SIJunta de Castilla y Leó

Generative Adversarial Network Image synthesis method for skin lesion Generation and classification

Skin cancer is the most commonly diagnosed cancer in today's growing population. One of the common limitations in the treatment of cancer is in the early detection of this disease. Mostly, skin cancer is detected in its later stages, when it has already compromised most of the skin area. Early detection of skin cancer is of utmost importance in increasing the chances for successful treatment, thus reducing mortality and morbidity. Currently, most dermatologists use a special microscope to examine the pattern and the affected area. This method is time-consuming and is prone to human errors, so there is a need for detecting skin cancer automatically. In this study, we investigate the automated classification of skin cancer using the Deep Convolution Generative Adversarial Network(DCGAN).In this work, Deep Convolutional GAN is used to generate realistic synthetic dermoscopic images, in a way that could enhance the classification performance in a large dataset and to evaluate whether the classification accuracy is enhanced or not, by generating a substantial amount of new skin lesion images. The DCGAN is trained using images generated by the Generator and then tweaked using the actual images and allow the Discriminator to make a distinction between fake and real images. The DCGAN might need slightly more fine-tuning to ripe a better return. Hyperparameter optimization can be utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters, namely number of iterations, batch size, and Learning rate can be tweaked, for example in this work we decreased the learning rate from the default 0.001 to 0.0002 and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models. Moreover, at each iteration in the course of the training process, the weights of the discriminative and generative network are updated to balance the loss between them. This pretraining and fine-tuning process is substantial for the model performance