Yucca: A Deep Learning Framework For Medical Image Analysis
This provides a more user-friendly and customizable platform for researchers and practitioners in medical imaging, though it is incremental as it builds on existing tools like PyTorch.
The authors tackled the lack of flexibility and modularity in deep learning frameworks for medical image analysis by introducing Yucca, an open-source framework that achieves state-of-the-art results in tasks like cerebral microbleeds detection and hippocampus segmentation.
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-friendliness. To address these challenges, we introduce Yucca, an open-source AI framework available at https://github.com/Sllambias/yucca, designed specifically for medical imaging applications and built on PyTorch and PyTorch Lightning. Yucca features a three-tiered architecture: Functional, Modules, and Pipeline, providing a comprehensive and customizable solution. Evaluated across diverse tasks such as cerebral microbleeds detection, white matter hyperintensity segmentation, and hippocampus segmentation, Yucca achieves state-of-the-art results, demonstrating its robustness and versatility. Yucca offers a powerful, flexible, and user-friendly platform for medical image analysis, inviting community contributions to advance its capabilities and impact.