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Yucca: A Deep Learning Framework For Medical Image Analysis

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arxiv 2407.19888 v1 pith:MPOLNOYT submitted 2024-07-29 cs.CV cs.AIcs.LGeess.IV

Yucca: A Deep Learning Framework For Medical Image Analysis

Sebastian N{\o}rgaard Llambias , Julia Machnio , Asbj{\o}rn Munk , Jakob Ambsdorf , Mads Nielsen , Mostafa Mehdipour Ghazi This is my paper
classification cs.CV cs.AIcs.LGeess.IV
keywords yuccamedicalanalysisimagedeepframeworkframeworkslearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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  1. Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

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    Self-supervised pretraining on 60K clinical-style brain MRIs improves out-of-domain generalization on classification, segmentation, and regression tasks, with hybrid objectives and small models showing strong results.

  2. Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

    cs.CV 2026-04 accept novelty 5.0

    Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited bene...