Yucca: A Deep Learning Framework For Medical Image Analysis

TL;DR

Yucca is a flexible, modular, and user-friendly deep learning framework built on PyTorch, designed specifically for medical image analysis, achieving state-of-the-art results across diverse tasks.

Contribution

It introduces a novel three-tiered architecture for medical imaging, enhancing flexibility, modularity, and ease of use compared to existing frameworks.

Findings

Achieves state-of-the-art results in multiple medical imaging tasks

Demonstrates robustness and versatility across diverse applications

Provides an open-source platform encouraging community contributions

Abstract

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.