Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3
This addresses automated melanoma detection for medical diagnostics, but it is incremental as it applies an existing method to a specific domain with suboptimal results.
The paper tackled skin lesion segmentation in dermoscopic images using DeepLab v3 with atrous convolution, achieving a mean Jaccard index of 0.498, which the authors acknowledge is not ideal but suggest could improve with further adjustments.
As melanoma diagnoses increase across the US, automated efforts to identify malignant lesions become increasingly of interest to the research community. Segmentation of dermoscopic images is the first step in this process, thus accuracy is crucial. Although techniques utilizing convolutional neural networks have been used in the past for lesion segmentation, we present a solution employing the recently published DeepLab 3, an atrous convolution method for image segmentation. Although the results produced by this run are not ideal, with a mean Jaccard index of 0.498, we believe that with further adjustments and modifications to the compatibility with the DeepLab code and with training on more powerful processing units, this method may achieve better results in future trials.