Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism
This work addresses skin cancer diagnosis by improving segmentation accuracy for medical imaging, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of automatic skin lesion segmentation from dermoscopic images, which is challenging due to poor contrast and unclear boundaries, and achieved state-of-the-art results by outperforming existing works and top-ranked models on the ISIC2018 and ISBI2017 datasets.
Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions.