  Unsupervised learning algorithms (e.g., self-supervised learning,
auto-encoder, contrastive learning) allow deep learning models to learn
effective image representations from large-scale unlabeled data. In medical
image analysis, even unannotated data can be difficult to obtain for individual
labs. Fortunately, national-level efforts have been made to provide efficient
access to obtain biomedical image data from previous scientific publications.
For instance, NIH has launched the Open-i search engine that provides a
large-scale image database with free access. However, the images in scientific
publications consist of a considerable amount of compound figures with
subplots. To extract and curate individual subplots, many different compound
figure separation approaches have been developed, especially with the recent
advances in deep learning. However, previous approaches typically required
resource extensive bounding box annotation to train detection models. In this
paper, we propose a simple compound figure separation (SimCFS) framework that
uses weak classification annotations from individual images. Our technical
contribution is three-fold: (1) we introduce a new side loss that is designed
for compound figure separation; (2) we introduce an intra-class image
augmentation method to simulate hard cases; (3) the proposed framework enables
an efficient deployment to new classes of images, without requiring resource
extensive bounding box annotations. From the results, the SimCFS achieved a new
state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation
Database. The source code of SimCFS is made publicly available at
https://github.com/hrlblab/ImageSeperation.
