Region of Interest Detection in Dermoscopic Images for Natural Data-augmentation

With the rapid growth of medical imaging research, there is a great interest in the automated detection of skin lesions with computer algorithms. The state-of-the-art datasets for skin lesions are often accompanied with very limited amount of ground truth labeling as it is laborious and expensive. The Region Of Interest (ROI) detection is vital to locate the lesion accurately and must be robust to subtle features of different skin lesion types. In this work, we propose the use of two object localization meta-architectures for end-to-end ROI skin lesion detection in dermoscopic images. We trained the Faster-RCNN-InceptionV2 and SSD-InceptionV2 on the ISBI-2017 training dataset and evaluated their performance on the ISBI-2017 testing set, PH2 and HAM10000 datasets. Since there was no earlier work in ROI detection for skin lesion with CNNs, we compared the performance of skin localization methods with the state-of-the-art segmentation method. The localization methods proved superior to the segmentation method in ROI detection on skin lesion datasets. In addition, based on the detected ROI, an automated natural data-augmentation method is proposed and used as pre-processing in the lesion diagnosis and segmentation task. To further demonstrate the potential of our work, we developed a real-time smart-phone application for automated skin lesions detection.