dc.contributor.author |
Goyal, Manu |
|
dc.contributor.author |
Ng, Jiahua |
|
dc.contributor.author |
Oakley, Amanda |
|
dc.contributor.author |
Yap, Moi Hoon |
|
dc.date.accessioned |
2022-05-04T23:11:13Z |
|
dc.date.available |
2022-05-04T23:11:13Z |
|
dc.date.issued |
2019-03-15 |
|
dc.identifier.citation |
(2019). Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 10953, 109530q-109530q-7-. |
|
dc.identifier.isbn |
9781510625532 |
|
dc.identifier.issn |
1605-7422 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/58945 |
|
dc.description.abstract |
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. |
|
dc.publisher |
SPIE |
|
dc.relation.ispartof |
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging |
|
dc.relation.ispartofseries |
Progress in Biomedical Optics and Imaging |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights |
Copyright 2019 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://www.spiedigitallibrary.org/article-sharing-policies?SSO=1 |
|
dc.subject |
Clinical Research |
|
dc.subject |
4.1 Discovery and preclinical testing of markers and technologies |
|
dc.subject |
Skin |
|
dc.title |
Skin lesion boundary segmentation with fully automated deep extreme cut methods |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1117/12.2513015 |
|
pubs.begin-page |
109530q-109530q-7 |
|
pubs.volume |
10953 |
|
dc.date.updated |
2022-04-18T20:50:28Z |
|
dc.rights.holder |
Copyright: Society of Photo‑Optical Instrumentation Engineers (SPIE) |
en |
pubs.finish-date |
2019-02-21 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2019-02-16 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.elements-id |
852096 |
|
pubs.org-id |
Medical and Health Sciences |
|
pubs.org-id |
School of Medicine |
|
pubs.org-id |
Medicine Department |
|
pubs.record-created-at-source-date |
2022-04-19 |
|