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[2602.21033] MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Summary
MIP Candy is a modular framework built on PyTorch for medical image processing, offering a flexible pipeline for data handling, training, and evaluation, while simplifying integration and customization for researchers.
Why It Matters
Medical image processing requires specialized tools to manage complex data and workflows. MIP Candy addresses the limitations of existing frameworks by providing a modular, user-friendly solution that enhances research efficiency and adaptability, which is crucial in the rapidly evolving field of medical imaging.
Key Takeaways
- MIP Candy simplifies the medical image processing pipeline with a modular design.
- The framework allows for easy customization and integration of various components.
- Built-in features include k-fold cross-validation and automatic region-of-interest detection.
- It is open-source and compatible with Python 3.12 or later.
- The extensible ecosystem supports pre-built models and consistent training patterns.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21033 (cs) [Submitted on 24 Feb 2026] Title:MIP Candy: A Modular PyTorch Framework for Medical Image Processing Authors:Tianhao Fu, Yucheng Chen View a PDF of the paper titled MIP Candy: A Modular PyTorch Framework for Medical Image Processing, by Tianhao Fu and Yucheng Chen View PDF HTML (experimental) Abstract:Medical image processing demands specialized software that handles high-dimensional volumetric data, heterogeneous file formats, and domain-specific training procedures. Existing frameworks either provide low-level components that require substantial integration effort or impose rigid, monolithic pipelines that resist modification. We present MIP Candy (MIPCandy), a freely available, PyTorch-based framework designed specifically for medical image processing. MIPCandy provides a complete, modular pipeline spanning data loading, training, inference, and evaluation, allowing researchers to obtain a fully functional process workflow by implementing a single method, $\texttt{build_network}$, while retaining fine-grained control over every component. Central to the design is $\texttt{LayerT}$, a deferred configuration mechanism that enables runtime substitution of convolution, normalization, and activation modules without subclassing. The framework further offers built-in $k$-fold cross-validation, dataset inspection with automatic region-of-interest detection, deep supervision, exponential moving aver...
