Compound figures are numerous in scientific publications. They consist in figures containing multiple (more or less related) sub figures. In the context of medical scientific publications, compound figures account for a significant amount of visual data. To exploit the information from those compound figures, they need to be segmented in several sub figures as independent as possible.
The compound figure separation task is composed of several subtasks:
- Panel segmentation
- Panel splitting
- Label recognition
- Caption splitting
How to use
In order to be sure to fulfill the software requirements, it is best to work within a Python virtual environment.
# Create the virtual environment. python3 -m venv venv # Activate it. . venv/bin/activate # Make sure pip is up to date. pip install --upgrade pip # Install pytorch first. pip install torch # Install the required packages. pip install -r requirements.txt # Download the requirements for nltk. python -c "import nltk; nltk.download('punkt')"
It is possible to follow training using TensorBoard
tensorboard --logdir=compfigsep/<TASK_NAME>/output/ [--bind_all]
data module contains function dealing with the various data sources. Among other things, one can preview, load and export the different data sets.
utils, several functions are here to handle miscellaneous tasks.
* `utils.detectron_utils` * `utils.figure`
Different data sets are involved in this project.
Learn more by reading this README.md.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825292. This project is better known as the ExaMode project. The objectives of the ExaMode project are:
- Weakly-supervised knowledge discovery for exascale medical data.
- Develop extreme scale analytic tools for heterogeneous exascale multimodal and multimedia data.
- Healthcare & industry decision-making adoption of extreme-scale analysis and prediction tools.
For more information on the ExaMode project, please visit www.examode.eu.