PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908935052591104 |
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| author | MacRae, Daniel C. van der Hoek, Luuk van der Wal, Robert de Vette, Suzanne P. M. Neh, Hendrike Ma, Baoqiang van Ooijen, Peter M. A. van Dijk, Lisanne V. |
| author_facet | MacRae, Daniel C. van der Hoek, Luuk van der Wal, Robert de Vette, Suzanne P. M. Neh, Hendrike Ma, Baoqiang van Ooijen, Peter M. A. van Dijk, Lisanne V. |
| contents | Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these developments we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. Built using community-standard distributions (PyTorch and MONAI), PR3DICTR provides an open-access, flexible and convenient framework for prediction model development, with an explicit focus on classification using three-dimensional medical image data. By combining modular design principles and standardization, it aims to alleviate developmental burden whilst retaining adjustability. It provides users with a wealth of pre-established functionality, for instance in model architecture design options, hyper-parameter solutions and training methodologies, but still gives users the opportunity and freedom to ``plug in'' their own solutions or modules. PR3DICTR can be applied to any binary or event-based three-dimensional classification task and can work with as little as two lines of code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03203 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction MacRae, Daniel C. van der Hoek, Luuk van der Wal, Robert de Vette, Suzanne P. M. Neh, Hendrike Ma, Baoqiang van Ooijen, Peter M. A. van Dijk, Lisanne V. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these developments we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. Built using community-standard distributions (PyTorch and MONAI), PR3DICTR provides an open-access, flexible and convenient framework for prediction model development, with an explicit focus on classification using three-dimensional medical image data. By combining modular design principles and standardization, it aims to alleviate developmental burden whilst retaining adjustability. It provides users with a wealth of pre-established functionality, for instance in model architecture design options, hyper-parameter solutions and training methodologies, but still gives users the opportunity and freedom to ``plug in'' their own solutions or modules. PR3DICTR can be applied to any binary or event-based three-dimensional classification task and can work with as little as two lines of code. |
| title | PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.03203 |