Overview of the ImageCLEFmed 2020 concept prediction task : medical image understanding

Pelka, Obioma (University of Applied Sciences and Arts Dortmund, Germany ; University Hospital Essen, Germany) ; Friedrich, Christoph M. (University of Applied Sciences and Arts Dortmund, Germany ; University Hospital Essen, Germany) ; García Seco de Herrera, Alba (University of Essex, UK) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland)

This paper describes the ImageCLEFmed 2020 Concept Detection Task. After _rst being proposed at ImageCLEF 2017, the medical task is in its 4th edition this year, as the automatic detection from medical images still remains a challenging task. In 2020, the format remained the same as in 2019, with a single sub-task. The concept detection task is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. Similar to the 2019 edition, the data set focuses on radiology images rather than biomedical images, however with an increased number of images. The distributed images were extracted from the biomedical open access literature (PubMed Central). The development data consists of 65,753 training and 15,970 validation images. Each image has corresponding Uni_ed Medical Language System (UMLS R) concepts, that were extracted from the original article image captions. In this edition, additional imaging acquisition technique labels were included in the distributed data, which were adopted for pre-_ltering steps, concept selection and ensemble algorithms. Most applied approaches for the automatic detection of concepts were deep learning based architectures. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classi_cation models were adopted. The performances of the submitted models (best score 0.3940) were evaluated using F1-scores computed per image and averaged across all 3,534 test images.


Note: Due to the COVID-19 outbreak, the CLEF 2020 - Conference and Labs of the Evaluation Forum venue in Thessaloniki was cancelled. The proceedings of the online conference are however published according to the original schedule.


Keywords:
Conference Type:
published full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Thessaloniki, Greece, 22-25 September 2020
Date:
2020-09
Thessaloniki, Greece
22-25 September 2020
Pagination:
16 p.
Published in:
Proceedings of the CLEF 2020 - Conference and labs of the evaluation forum
External resources:
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 Record created 2020-11-18, last modified 2020-11-20

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