Analysing TB severity levels with an enhanced deep residual learning– depth-resnet
Conference paper
Gao, X., James-Reynolds, C. and Currie, E. 2018. Analysing TB severity levels with an enhanced deep residual learning– depth-resnet. Cappellato, L., Ferro, N., Nie, J-Y. and Soulier, L. (ed.) CLEF 2018 Conference and Labs of the Evaluation Forum - ImageCLEF-Multimedia Retrieval in CLEF. Avignon, France 10 - 14 Sep 2018 CEUR-WS.
Type | Conference paper |
---|---|
Title | Analysing TB severity levels with an enhanced deep residual learning– depth-resnet |
Authors | Gao, X., James-Reynolds, C. and Currie, E. |
Abstract | This work responds to the Competition of Tuberculosis Task organised by imageCLEF 2018. While Task #3 appears to be challenging, the experience was very enjoyable. If time had been permitted, it was certain that more accurate results could have been achieved. The authors submitted 2 runs. Based on the given training datasets with severity levels of 1 to 5, an enhanced deep residual learning architecture, depthResNet, is developed and applied to train the datasets to classify 5 categories. The datasets are pre-processed with each volume being segmented into twenty- 128×128×depth blocks with ~64 pixel overlaps. While each block has been predicted with a severity level, assembling all constituent block scores together to give an overall label for the concerned volume tends to be more challenging. Since the probability of high severity is not provided from the training datasets, which bears little resemblance to the classification probability, the submission of probability for the first run was manually assigned as 0.9, 0.7, 0.5, 0.3, and 0.1 to severity levels of 1 to 5 respectively. After the deadline was extended, the model was re-trained with frame numbers increased from 1 to 8, which takes much longer to train. In addition, a new measure was introduced to calculate the overall probability of high severity based on the block scores. As a result, with regard to classification accuracy, the 2nd submitted run achieved place 14 over a total of 36 submissions, a significant |
Conference | CLEF 2018 Conference and Labs of the Evaluation Forum - ImageCLEF-Multimedia Retrieval in CLEF |
Proceedings Title | CLEF 2018 Working Notes: Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018. |
Series | CEUR Workshop Proceedings |
Editors | Cappellato, L., Ferro, N., Nie, J-Y. and Soulier, L. |
ISSN | 1613-0073 |
Publisher | CEUR-WS |
Publication dates | |
Online | 24 Jul 2018 |
Publication process dates | |
Deposited | 21 Aug 2018 |
Accepted | 20 Jun 2018 |
Output status | Published |
Publisher's version | |
Web address (URL) | http://ceur-ws.org/Vol-2125/paper_175.pdf |
Scopus EID | 2-s2.0-85051085657 |
Web address (URL) of conference proceedings | https://ceur-ws.org/Vol-2125/ |
Related Output | |
Has metadata | http://www.scopus.com/inward/record.url?eid=2-s2.0-85051085657&partnerID=MN8TOARS |
Language | English |
https://repository.mdx.ac.uk/item/87wvv
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