A Multi-task Framework for Skin Lesion Detection and Segmentation
Vesal S, Patil SM, Ravikumar N, Maier A (2018)
Publication Type: Conference contribution
Publication year: 2018
Publisher: Springer Verlag
Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Book Volume: 11041 LNCS
Pages Range: 285-293
Conference Proceedings Title: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis
Event location: Granada, Spain
ISBN: 978-3-030-01201-4
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Vesal18-AMF.pdf
DOI: 10.1007/978-3-030-01201-4_31
Abstract
Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is time consuming and subject to intra- and inter-observer variations among dermatologists. This underlines the need for an accurate and automatic approach to skin lesion segmentation. To tackle this issue, we propose a multi-task convolutional neural network (CNN) based, joint detection and segmentation framework, designed to initially localize the lesion and subsequently, segment it. A ‘Faster region-based convolutional neural network’ (Faster-RCNN) which comprises a region proposal network (RPN), is used to generate bounding boxes/region proposals, for lesion localization in each image. The proposed regions are subsequently refined using a softmax classifier and a bounding-box regressor. The refined bounding boxes are finally cropped and segmented using ‘SkinNet’, a modified version of U-Net. We trained and evaluated the performance of our network, using the ISBI 2017 challenge and the PH2 datasets, and compared it with the state-of-the-art, using the official test data released as part of the challenge for the former. Our approach outperformed others in terms of Dice coefficients (>0.93), Jaccard index (>0.88), accuracy (>0.96) and sensitivity (>0.95), across five-fold cross validation experiments.
Authors with CRIS profile
Sulaiman Vesal
Lehrstuhl für Informatik 5 (Mustererkennung)
Nishant Ravikumar
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
India (IN)How to cite
APA:
Vesal, S., Patil, S.M., Ravikumar, N., & Maier, A. (2018). A Multi-task Framework for Skin Lesion Detection and Segmentation. In OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis (pp. 285-293). Granada, Spain: Springer Verlag.
MLA:
Vesal, Sulaiman, et al. "A Multi-task Framework for Skin Lesion Detection and Segmentation." Proceedings of the International Workshop on Skin Image Analysis(ISIC 2018, Held in Conjunction with MICCAI 2018), Granada, Spain Springer Verlag, 2018. 285-293.
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