BINDER: On Identification and Retrieval of Near-Duplicate Biological Images: a New Dataset and Protocol
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Abstract: Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers but also for the research community in general. In this work, we introduce BINDER – Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches – the training set – as well as 1,821 patches – split in validation and test sets – with accompanying manipulations obtained by means of transformations including rotation, translation, scale, perspective transform, contrast adjustment, and/or compression artifacts. In addition, we show how novel adaptations of existing image retrieval and metric learning models, when trained on this dataset, can be applied to achieve high-accurac' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["BINDER","Biomedical images","Image retrieval validation","Metric learning models","Model training","Near-duplicate image identification"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Abstract: Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers but also for the research community in general. In this work, we introduce BINDER – Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches – the training set – as well as 1,821 patches – split in validation and test sets – with accompanying manipulations obtained by means of transformations including rotation, translation, scale, perspective transform, contrast adjustment, and/or compression artifacts. In addition, we show how novel adaptations of existing image retrieval and metric learning models, when trained on this dataset, can be applied to achieve high-accurac' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Abstract: Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers but also for the research community in general. In this work, we introduce BINDER – Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches – the training set – as well as 1,821 patches – split in validation and test sets – with accompanying manipulations obtained by means of transformations including rotation, translation, scale, perspective transform, contrast adjustment, and/or compression artifacts. In addition, we show how novel adaptations of existing image retrieval and metric learning models, when trained on this dataset, can be applied to achieve high-accurac
BINDER; Biomedical images; Image retrieval validation; Metric learning models; Model training; Near-duplicate image identification