Compound Figure Separation of Biomedical Images with Side Loss

Yao, Tianyuan; Qu, Chang; Liu, Quan; Deng, Ruining; Tian, Yuanhan; Xu, Jiachen; Jha, Aadarsh; Bao, Shunxing; Zhao, Mengyang; Fogo, Agnes B.; Landman, Bennett A.; Chang, Catie; Yang, Haichun; Huo, Yuankai

doi:10.48550/arxiv.2107.08650

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Practical cross-area applications can be found in works like (Yao et al 2021), where a method is proposed to automatically separate compound figures in biomedical research articles. It uses a deep learning model that is trained to separate the subfigures based on their visual features and is augmented with a "side loss" to ensure that the model also considers the context and layout of the subfigures.…”

Section: Discussionmentioning

confidence: 99%

Deepfakes: evolution and trends

et al. 2023

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This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

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Section: Discussionmentioning

confidence: 99%

Deepfakes: evolution and trends

et al. 2023

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“…This work is extended from our conference paper (Yao et al, 2021) with the new efforts listed below: (1) we included more technical and evaluation details for the proposed method;…”

Section: Introductionmentioning

confidence: 99%

Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

Yao

Long

et al. 2022

Melba

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With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at scale can be challenging for individual labs. Existing online resources, such as digital books, publications, and search engines, provide a new resource for obtaining large-scale images. However, published images in healthcare (e.g., radiology and pathology) consist of a considerable amount of compound figures with subplots. In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation. Our technical contribution is four-fold: (1) we introduce a simulation-based training framework that minimizes the need for resource extensive bounding box annotations; (2) we propose a new side loss that is optimized for compound figure separation; (3) we propose an intra-class image augmentation method to simulate hard cases; and (4) to the best of our knowledge, this is the first study that evaluates the efficacy of leveraging self-supervised learning with compound image separation. From the results, the proposed SimCFS achieved state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The pretrained self-supervised learning model using large-scale mined figures improved the accuracy of downstream image classification tasks with a contrastive learning algorithm. The source code of SimCFS is made publicly available at <a href='https://github.com/hrlblab/ImageSeperation'>https://github.com/hrlblab/ImageSeperation</a>

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“…This work is extended from our conference paper (Yao et al, 2021) with the new efforts listed below: (1) we included more technical and evaluation details for the proposed method;…”

Section: Introductionmentioning

confidence: 99%

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2022

Melba

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Compound Figure Separation of Biomedical Images with Side Loss

Cited by 3 publications

References 28 publications

Deepfakes: evolution and trends

Deepfakes: evolution and trends

Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

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