# Yucca: A Deep Learning Framework For Medical Image Analysis

arXiv: https://arxiv.org/abs/2407.19888 · HTML: https://mlsanity.com/paper/2407.19888/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging](https://mlsanity.com/paper/2308.04395/) (2023)
- [Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19](https://mlsanity.com/paper/2301.09322/) (2023)

## Built on by (most cited first, 5 of 5)

- [AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation](https://mlsanity.com/paper/2408.00640/) (2024)
- [Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge](https://mlsanity.com/paper/2604.11679/) (2026)
- [Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models](https://mlsanity.com/paper/2501.14051/) (2025)
- [Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning](https://mlsanity.com/paper/2506.22041/) (2025)
- [ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine](https://mlsanity.com/paper/2603.14255/) (2026)

## Abstract (arXiv)

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.