mlsanityData to 14 Oct 2026

Yucca: A Deep Learning Framework For Medical Image Analysis

arXiv 2407.19888 ↗First posted 29 Jul 2024cs.CV cs.AI cs.LGcited by 5

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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

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  3. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  4. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
  5. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  6. 2022FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Braincited by 10
  7. 2023Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imagingcited by 3
  8. 2023Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.