Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

Ionescu, Bogdan; Müller, Henning; Villegas, Mauricio; Herrera, Alba García Seco de; Eickhoff, Carsten; Andrearczyk, Vincent; Cid, Yashin Dicente; Liauchuk, Vitali; Kovalev, Vassili; Hasan, Sadid A.; Yuan, Ling; Farri, Oladimeji; Liu, Joey; Lungren, Matthew P.; Dang-Nguyen, Duc-Tien; Piras, Luca; Riegler, Michael; Zhou, Liting; Lux, Mathias; Gurrin, Cathal

doi:10.1007/978-3-319-98932-7_28

Cited by 30 publications

(22 citation statements)

References 20 publications

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“…Existing studies attempted to adapt advanced general VQA methods based on large-scale pre-trained models for Med-VQA [1,2,38,42,48]. These studies are mostly reports of the ImageCLEF VQA-Med Challenge [2,16]. Typically, visual features are extracted by deep pre-trained architectures such as ResNet or VGGNet, and textual features are extracted by stacked RNN-based layers.…”

Section: Medical Visual Question Answeringmentioning

confidence: 99%

Medical Visual Question Answering via Conditional Reasoning

Zhan

Liu

Lü

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Medical visual question answering (Med-VQA) aims to accurately answer a clinical question presented with a medical image. Despite its enormous potential in healthcare industry and services, the technology is still in its infancy and is far from practical use. Med-VQA tasks are highly challenging due to the massive diversity of clinical questions and the disparity of required visual reasoning skills for different types of questions. In this paper, we propose a novel conditional reasoning framework for Med-VQA, aiming to automatically learn effective reasoning skills for various Med-VQA tasks. Particularly, we develop a question-conditioned reasoning module to guide the importance selection over multimodal fusion features. Considering the different nature of closed-ended and open-ended Med-VQA tasks, we further propose a type-conditioned reasoning module to learn a different set of reasoning skills for the two types of tasks separately. Our conditional reasoning framework can be easily applied to existing Med-VQA systems to bring performance gains. In the experiments, we build our system on top of a recent state-of-the-art Med-VQA model and evaluate it on the VQA-RAD benchmark [23]. Remarkably, our system achieves significantly increased accuracy in predicting answers to both closed-ended and open-ended questions, especially for open-ended questions, where a 10.8% increase in absolute accuracy is obtained. The source code can be downloaded from https://github.com/awenbocc/med-vqa. CCS CONCEPTS • Computing methodologies → Computer vision tasks.

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Section: Medical Visual Question Answeringmentioning

confidence: 99%

Medical Visual Question Answering via Conditional Reasoning

Zhan

Liu

Lü

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…Dynamic Search for Complex Tasks 7 The lab strives to answer one key question: how can we evaluate, and consequently build, dynamic search algorithms? The 2018 Lab focuses on the development of an evaluation framework, where participants submit "querying agents" that generate queries to be submitted to a static retrieval system.…”

Section: The Clef Lab Sessionsmentioning

confidence: 99%

“…ImageCLEF 9 organizes three main tasks and a pilot task: (i) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (ii) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (iii) a lifelog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (iv) a pilot task on visual question answering where systems are asked to answer medical questions [7].…”

Section: The Clef Lab Sessionsmentioning

confidence: 99%

Report on CLEF 2018

et al. 2019

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This is a report on the ninth edition of the Conference and Labs of the Evaluation Forum (CLEF 2018), held in early September 2018, in Avignon, France. CLEF was a four day event combining a Conference and an Evaluation Forum. The Conference featured keynotes by Nicholas Belkin, Julio Gonzalo, and Gabriella Pasi, and presentation of 29 peer reviewed research papers covering a wide range of topics in addition to many posters. The Evaluation Forum consisted to ten Labs: CENTRE, CheckThat, DynSe, eRisk, eHealth, ImageCLEF, LifeCLEF, Cultural Microblog Contextualization, PAN, and PIR-CLEF, addressing a wide range of tasks, media, languages, and ways to go beyond standard test collections.

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“…It has been held every year since then and delivered many results in the analysis and retrieval of images [20,21]. Medical tasks started in 2004 and have in some years been the majority of the tasks in ImageCLEF [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

Ionescu

Müller

Péteri

et al. 2020

Lecture Notes in Computer Science

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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

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Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

Cited by 30 publications

References 20 publications

Medical Visual Question Answering via Conditional Reasoning

Medical Visual Question Answering via Conditional Reasoning

Report on CLEF 2018

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

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