Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign

ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started 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 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 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 will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020

ImageCLEF 2022: Multimedia Retrieval in Medical, Nature, Fusion, and Internet Applications

ImageCLEF is part of the Conference and Labs of the Evaluation Forum (CLEF) since 2003. CLEF 2022 will take place in Bologna, Italy. ImageCLEF is an ongoing evaluation initiative which 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 its 20th edition, ImageCLEF will have four main tasks: (i) a Medical task addressing concept annotation, caption prediction, and tuberculosis detection; (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images; (iii) an Aware task addressing the prediction of real-life consequences of online photo sharing; and (iv) a new Fusion task addressing late fusion techniques based on the expertise of the pool of classifiers. In 2021, over 100 research groups registered at ImageCLEF with 42 groups submitting more than 250 runs. These numbers show that, despite the COVID-19 pandemic, there is strong interest in the evaluation campaign

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

This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum — CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 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 2021, the 19th edition of ImageCLEF will organize four main tasks: (i) a Medical task addressing visual question answering, a concept annotation and a tuberculosis classification task, (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images, (iii) a DrawnUI task addressing the creation of websites from either a drawing or a screenshot by detecting the different elements present on the design and a new (iv) Aware task addressing the prediction of real-life consequences of online photo sharing. The strong participation in 2020, despite the COVID pandemic, with over 115 research groups registering and 40 submitting over 295 runs for the tasks shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2021

Overview of the ImageCLEF 2021: Multimedia Retrieval in Medical, Nature, Internet and Social Media Applications

This paper presents an overview of the ImageCLEF 2021 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2021. 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 2021, the 19th 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 nature coral task about segmenting and labeling collections of coral reef images, (iii) an Internet task addressing the problems of identifying hand-drawn and digital user interface components, and (iv) a new social media aware task on estimating potential real-life effects of online image sharing. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 38 groups submitting more than 250 runs

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started 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 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign

Lung tissue analysis: from local visual descriptors to global modeling

Medical imaging plays an important role in patient diagnosis and treatment planning. A standard procedure to assess a respiratory disease is a CT scan of the chest, where radiologists can detect subtle alterations in the lung tissue. This thesis aims at describing the lung tissue in CT scans, both from a local and a global perspective. It explores all the steps involved in the pipeline for the automatic analysis of the lung tissue: the initial lung segmentation, the division of lung fields into subregions, the extraction of local biomedical features, and the assembly of local features to form a global model. A new tissue descriptor is presented, as well as a novel graph-based model that provides a global characterization of the lung tissue. In addition, this thesis describes a new on-line platform where clinicians can extract state-of-the-art computerized image-based features

Texture–based graph model of the lungs for drug resistance detection, tuberculosis type classification, and severity scoring ::participation in the ImageCLEF 2018 Tuberculosis Task

In 2018, ImageCLEF proposed a task using CT (Computed Tomography) scans of patients with tuberculosis (TB). The task was divided into three subtasks: multi{drug resistance detection, TB type classi _cation, and severity scoring. In this work we present a graph model of the lungs capable of characterizing TB patients with di_erent lung problems. The graph contains a _xed number of nodes with weighted edges based on dissimilarity measures between texture descriptors computed in the nodes. This model encodes the texture distribution along the lungs, making it suitable for describing patients with di_erent TB types. The results show the strength of the technique, leading to high results in the three subtasks

Textured graph-model of the lungs for tuberculosis type classification and drug resistance prediction ::participation in ImageCLEF 2017

In 2017, the ImageCLEF benchmark proposed a task based on CT (Computed Tomography) images of patients with tuberculosis (TB). This task was divided into two subtasks: multi-drug resistance prediction, and TB type detection. In this work we present a graph-model of the lungs capable of characterizing TB patients with different lung problems. This graph contains a fixed number of nodes with weighted edges based on distance measures between texture descriptors computed on the nodes. This model attempts to encode the texture distribution along the lungs, making it suitable for describing patients with different tuberculosis types. The results show the strength of the technique, leading to best results in the competition for multi-drug resistance (AUC = 0.5825) and good results in the tuberculosis type detection (Cohen’s Kappa coef. = 0.1623), with many of the good runs being fairly close