Yucca: A Deep Learning Framework For Medical Image Analysis
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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.
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
8 of 17 references.
References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
- 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
- 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
- 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
- 2022FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Braincited by 10
- 2023Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imagingcited by 3
- 2023Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19cited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2024AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentationcited by 17
- 2026Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challengecited by 3
- 2025Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Modelscited by 0
- 2025Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learningcited by 0
- 2026ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEnginecited by 0
- 2026Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge13 Apr 2026
- 2026ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine15 Mar 2026
- 2025Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning27 Jun 2025
- 2025Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models23 Jan 2025
- 2024AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation1 Aug 2024