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

• URL: http://arxiv.org/abs/2208.14357v1
• Date: Tue, 30 Aug 2022 16:02:34 GMT
• Title: Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
• Authors: Tianyuan Yao, Chang Qu, Jun Long, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Zuhayr Asad, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A.Landman, Haichun Yang, Catie Chang, Yuankai Huo
• Abstract summary: We introduce a simulation-based training framework that minimizes the need for resource extensive bounding box annotations. We also propose a new side loss that is optimized for compound figure separation. This is the first study that evaluates the efficacy of leveraging self-supervised learning with compound image separation.
• Score: 12.445324044675116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 https://github.com/hrlblab/ImageSeperation.

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