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The CL-SciSumm Shared Task 2018: Results and Key Insights
This overview describes the official results of the CL-SciSumm Shared Ta...
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Overview and Results: CL-SciSumm Shared Task 2019
The CL-SciSumm Shared Task is the first medium-scale shared task on scie...
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Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
Medical images can be decomposed into normal and abnormal features, whic...
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Stacked Autoencoders for Medical Image Search
Medical images can be a valuable resource for reliable information to su...
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Catching Out-of-Context Misinformation with Self-supervised Learning
Despite the recent attention to DeepFakes and other forms of image manip...
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IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
Medicine is an important application area for deep learning models. Rese...
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TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
In this paper, we introduce the semantic knowledge of medical images fro...
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MedICaT: A Dataset of Medical Images, Captions, and Textual References
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75 text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74 manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.
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