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

APRIL is overexpressed in cancer: link with tumor progression

<p>Abstract</p> <p>Background</p> <p>BAFF and APRIL share two receptors – TACI and BCMA – and BAFF binds to a third receptor, BAFF-R. Increased expression of BAFF and APRIL is noted in hematological malignancies. BAFF and APRIL are essential for the survival of normal and malignant B lymphocytes, and altered expression of BAFF or APRIL or of their receptors (BCMA, TACI, or BAFF-R) have been reported in various B-cell malignancies including B-cell non-Hodgkin's lymphoma, chronic lymphocytic leukemia, Hodgkin's lymphoma, multiple myeloma, and Waldenstrom's macroglobulinemia.</p> <p>Methods</p> <p>We compared the expression of <it>BAFF, APRIL, TACI and BAFF-R </it>gene expression in 40 human tumor types – brain, epithelial, lymphoid, germ cells – to that of their normal tissue counterparts using publicly available gene expression data, including the Oncomine Cancer Microarray database.</p> <p>Results</p> <p>We found significant overexpression of <it>TACI </it>in multiple myeloma and thyroid carcinoma and an association between TACI expression and prognosis in lymphoma. Furthermore, <it>BAFF and APRIL </it>are overexpressed in many cancers and we show that <it>APRIL </it>expression is associated with tumor progression. We also found overexpression of at least one proteoglycan with heparan sulfate chains (HS), which are coreceptors for APRIL and TACI, in tumors where APRIL is either overexpressed or is a prognostic factor. APRIL could induce survival or proliferation directly through HS proteoglycans.</p> <p>Conclusion</p> <p>Taken together, these data suggest that APRIL is a potential prognostic factor for a large array of malignancies.</p

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

Sensitivity of the meridional transport in a 1.5-layer ocean model to localized mass sources

The response of a 1.5-layer ocean model forced by localized stochastic mass sources is studied. The focus is on the sensitivity of the spectral characteristics of the meridional transport to the location and the extent of the source region. In all the experiments, performed in hemispheric and interhemispheric basins, the spectra show a peak at interannual time scale revealing the existence of an oscillation. The period of the oscillation is defined by the zonal extent of the forcing, whereas its amplitude is affected by its location. When the source region is located in the northwestern corner of the basin, the peak emerges clearly on the spectrum of the meridional transport, whereas it is strongly reduced when the source region is located in open ocean. The extension to an inter-hemispheric basin increases the energy at the period of the oscillation, but the introduction of the equatorial dynamics does not affect the spectral characteristics of the response for periods longer than 1 year

Temperature and salinity anomalies in the North Atlantic subpolar gyre

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Origin of the Annual to Decadal Peaks of Variability in the Response of Simple Ocean Models to Stochastic Forcing

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Overview of the imageCLEF 2022 aware task

International audienceThe paper presents the overview of the ImageCLEF 2022 Aware task whose final objective is to make users more aware about the consequences of posting information on social networks. This is important insofar as users are often unaware about the effects of personal data sharing. Focus is put on modeling the impact of sharing impactful real-life situations such as searching for a bank loan, an accommodation, or a job. Since photos are one of the main types of data shared online, the task is instantiated as a photographic user profile assessment. Participants receive a training and validation dataset which includes a set of photographic profiles which are manually rated for each situation. They are required to train algorithms which rate and then rank test profiles in each tested situation. The correlation between automatic and manual profile rankings is used to measure the performance of algorithms. The overview discusses the task settings, the dataset constitution process, and the approaches proposed this year

Raising user awareness about the consequences of online photo sharing

International audienceOnline social networks use AI techniques to automatically infer profiles from users' shared data. However, these inferences and their effects remain, to a large extent, opaque to the users themselves. We propose a method which raises user awareness about the potential use of their profiles in impactful situations, such as searching for a job or an accommodation. These situations illustrate usage contexts that users might not have anticipated when deciding to share their data. User photographic profiles are described by automatic object detections in profile photos, and associated object ratings in situations. Human ratings of the profiles per situation are also available for training.These data are represented as graph structures which are fed into graph neural networks in order to learn how to automatically rate them. An adaptation of the learning procedure per situation is proposed since the same profile is likely to be interpreted differently, depending on the context. Automatic profile ratings are compared to one another in order to inform individual users of their standing with respect to others. Our method is evaluated on a public dataset, and consistently outperforms competitive baselines. An ablation study gives insights about the role of its main components

Face verification with challenging imposters and diversified demographics

International audienceFace verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent years gives the impression that the task is practically solved. Here, we revisit the problem and argue that existing evaluation datasets were built using two oversimplifying design choices. First, the usual identity selection to form imposter pairs is not challenging enough because, in practice, verification is needed to detect challenging imposters. Second, the underlying demographics of existing datasets are often insufficient to account for the wide diversity of facial characteristics of people from across the world. To mitigate these limitations, we introduce the FaVCI2D dataset. Imposter pairs are challenging because they include visually similar faces selected from a large pool of demographically diversified identities. The dataset also includes metadata related to gender, country and age to facilitate fine-grained analysis of results. FaVCI2D is generated from freely distributable resources and is compliant with data protection regulations. Experiments with state-of-the-art deep models that provide nearly 100% performance on existing datasets show a significant performance drop for FaVCI2D, confirming our starting hypothesis. Equally important, we analyze legal and ethical challenges which appeared in recent years and hindered the development of face analysis research. We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer

Presentation and evaluation of the IPSL‐CM6A‐LR Ensemble of extended historical simulations

International audienceThe Institut Pierre-Simon Laplace Climate Modeling Center has produced an ensemble of extended historical simulations using the IPSL-CM6A-LR climate model. This ensemble (referred to as IPSL-EHS) is composed of 32 members over the 1850–2059 period that share the same external forcings but differ in their initial conditions. In this study, we assess the simulated decadal to multidecadal climate variability in the IPSL-EHS. In particular, we examine the global temperature evolution and recent warming trends, and their consistency with ocean heat content and sea ice cover. The model exhibits a large low-frequency internal climate variability. In particular, a quasi-bicentennial mode of internal climate variability is present in the model and is associated with the Atlantic Meridional Overturning Circulation. Such variability modulates the global mean surface air temperature changes over the historical period by about $\sim$0.1K. This modulation is found to be linked to the phase present in the initial condition state of each member. This variability appears to decrease during the 1850–2018 period in response to external forcings. The analysis of the ocean heat content reveals furthermore an overestimation of the ocean stratification, which likely leads to an overestimation of the recent warming rate on averag