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Overview of the ImageCLEFmed 2019 concept detection task

Pelka, O and Friedrich, CM and Seco De Herrera, AG and Müller, H (2019) Overview of the ImageCLEFmed 2019 concept detection task. In: CLEF 2019 - Conference and Labs of the Evaluation Forum, 2019-09-09 - 2019-09-12, Lugano, Switzerland.

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This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medical caption task, after it was first proposed in ImageCLEF 2017. Concept detection from medical images remains a challenging task. In 2019, the format changed to a single subtask and it is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. To reduce noisy labels and limit variety, the data set focuses solely on radiology images rather than biomedical figures, extracted from the biomedical open access literature (PubMed Central). The development data consists of 56,629 training and 14,157 validation images, with corresponding Unified Medical Language System (UMLSR) concepts, extracted from the image captions. In 2019 the participation is higher, regarding the number of participating teams as well as the number of submitted runs. Several approaches were used by the teams, mostly deep learning techniques. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classification models were the frequently used approaches. Evaluation uses F1-scores computed per image and averaged across all 10,000 test images.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: CEUR Workshop Proceedings
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 22 Jan 2020 14:49
Last Modified: 23 Sep 2022 19:34

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