Welcome to the KerasFuse project!
KerasFuse is an open-source Python library for medical image analysis tasks. It leverages the power of TensorFlow and Keras, combining them with various computer vision techniques to facilitate the development of deep learning models.
KerasFuse offers a comprehensive collection of modules, functions, layers, blocks, and models to empower researchers and developers in the field of medical image analysis. With KerasFuse, you can easily build and train deep learning models for tasks such as image segmentation, classification, and more.
We believe that collaboration is key to building a robust and efficient library, and we welcome contributions from the community. Whether you are a seasoned deep learning expert or just starting your journey, there are many ways you can contribute to KerasFuse:
If you have a strong background in TensorFlow, Keras, or deep learning in general, you can contribute to the development of KerasFuse. This includes implementing new layers, blocks, and models, optimizing existing code, improving performance, and fixing bugs. To get started, check out the project repository on GitHub and see the "Contributing" guidelines.
Clear and comprehensive documentation is crucial for any open-source project. If you have a knack for technical writing, you can contribute by improving the existing documentation, adding new examples, writing tutorials, or creating API references. Well-documented code and examples will help users quickly understand and utilize the capabilities of KerasFuse.
Real-world examples and pretrained models play a vital role in showcasing the capabilities of KerasFuse. If you have experience in medical image analysis or related domains, you can contribute by providing classification and segmentation examples using the KerasFuse models. Additionally, if you have pretrained models on datasets such as Kvasir, brain tumors, or other relevant medical image datasets, we encourage you to share them with the community.
Thorough testing ensures the reliability and stability of the library. By testing KerasFuse in various environments and scenarios, you can help identify and report bugs, compatibility issues, or performance bottlenecks. Additionally, providing feedback on user experience and suggesting improvements will be greatly appreciated.
To start contributing to KerasFuse, please follow these steps:
- Fork the KerasFuse repository on GitHub.
- Clone your forked repository to your local machine.
- Set up the development environment as outlined in the documentation.
- Pick an area of interest from the list above and start contributing.
- Submit your changes as a pull request, following the guidelines provided.
KerasFuse thrives on an active and engaged community. Joining the community allows you to collaborate with like-minded individuals, seek guidance, and stay up-to-date with the latest developments. Here's how you can get involved:
- Join our official mailing list or forum to ask questions and participate in discussions.
- Follow us on Twitter to receive updates and announcements about KerasFuse.
- Join our Slack channel to interact with the community in real-time.
Contributions to KerasFuse are highly valued, and we acknowledge the importance of recognition. As a contributor, your efforts will be duly recognized in the project's documentation, repository, and other relevant platforms. Moreover, your contributions will have a direct impact on the medical image analysis community and further advancements in the field.
We look forward to your contributions and appreciate your support in making KerasFuse a powerful tool for medical image analysis!
Happy coding!
The KerasFuse Team