Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications

Abstract

This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum -CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections B. Ionescu et al.of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs.

The 2021 ImageCLEF Benchmark: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

“…It has been held every year since then and delivered many results in the analysis and retrieval of images [12,15]. Medical tasks started in 2004 and have in some years been the majority of the tasks in ImageCLEF [10,11]. The objectives of ImageCLEF have always been the multilingual or language-independent analysis of visual content.…”

“…This underlines the importance of evaluation campaigns for disseminating best scientific practices. In the ImageCLEF 2020 campaign [11], 115 teams registered, 40 teams completed the tasks and submitted over 295 runs, despite the outbreak of the COVID-19 pandemic and lockdown during the benchmark. Although the number of registrations was lower than in 2019, the rate of the participants actually submitting runs increased by over 8%.…”

A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task

“…ImageCLEF organises 4 main tasks for the 2020 edition with a global objective of promoting the evaluation of technologies for annotation, indexing, and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and application domains, including medicine [4].…”

Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data

“…The current paper makes use of deep learning to automatically detect tuberculosis and related affections in lung CTs, in the context of the ImageClef Tuberculosis task [1,2]. We investigate three types of approaches, different with respect to the way the volumetric data is given as input to neural network-based classifiers.…”