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

Overview of ImageCLEFtuberculosis 2019 ::automatic CT-based report generation and tuberculosis severity assessment

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language-independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well as the retrieval of images. Since 2017, when the tuberculosis task started in ImageCLEF, the number of participants has kept growing. In 2019, 13 groups from 11 countries participated in at least one of the two subtasks proposed: (1) SVR subtask: the assessment of a tuberculosis severity score and (2) CTR subtask: the automatic generation of a CT report based on six relevant CT findings. In this second edition of the SVR subtask the results support the assessment of a severity score based on the CT scan with up to 0.79 area under the curve (AUC) and 74% accuracy, so very good results. In addition, in the first edition of the CTR subtask, impressive results were obtained with 0.80 average AUC and 0.69 minimum AUC for the six CT findings proposed

Overview of ImageCLEF tuberculosis 2020:automatic CT-based report generation

ImageCLEF is a part of the Conference and Labs of the Evaluation Forum (CLEF) initiative and presents a set of image information retrieval tasks. ImageCLEF was historically focused on the variety of multimodal image classification, retrieval and annotation tasks. The tuberculosis task started in ImageCLEF in 2017 and changed from year to year. This year’s edition was dedicated to the automatic generation of a lung-wise CT report (CTR) based on three relevant CT findings. This year 9 groups from 8 countries participated in the task and submitted results. This year’s task is similar to the CTR (CT Report) subtask from the previous year, so it is possible to compare the results almost directly. Impressive improvement of the results was obtained with 0.92 (+0.10) average Area Under ROC-curve (AUC) and 0.89 (+0.20) minimum AUC for the three CT findings proposed