Dr. Amanda Oakley Profile

Dr. Amanda Oakley

at DermNet

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Proceedings Article | 15 March 2019 Presentation + Paper

Skin lesion boundary segmentation with fully automated deep extreme cut methods

Manu Goyal, Jiahua Ng, Amanda Oakley, Moi Hoon Yap

Proceedings Volume 10953, 109530Q (2019) https://doi.org/10.1117/12.2513015

KEYWORDS: Image segmentation, Skin, Computer aided diagnosis and therapy, Melanoma, Computing systems, Skin cancer, Medical imaging, CAD systems, Binary data, Medicine

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The skin is the largest organ in our body. There is a high prevalence of skin diseases and a scarcity of dermatologists, the experts in diagnosing and managing skin diseases, making CAD (Computer Aided Diagnosis) of skin disease an important field of research. Many patients present with a skin lesion of concern, to determine if it is benign or malignant. Lesion diagnosis is currently performed by dermatologists taking a history and examining the lesion and the entire body surface with the aid of a dermatoscope. Automatic lesion segmentation and evaluation of the symmetry or asymmetry of structures and colors with the help of computers may classify a lesion as likely benign or as likely malignant. We have explored a deep learning program called Deep Extreme Cut (DEXTR) and used the Faster-RCNN-InceptionV2 network to determine extreme points (left-most, right-most, top and bottom pixels). We used the ISIC challenge-2017 images for the training set and received Jaccard index of 82.2% on the ISIC testing set 2017 and 85.8% on the PH2 dataset. The proposed method outperformed the winner algorithm of the competition by 5.7% for the Jaccard index.

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