MIP Candy

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, build_network, while retaining fine-grained control over every component. Central to the design is 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 average, multi-frontend experiment tracking (Weights & Biases, Notion, MLflow), training state recovery, and validation score prediction via quotient regression. An extensible bundle ecosystem provides pre-built model implementations that follow a consistent trainer–predictor pattern and integrate with the core framework without modification. MIPCandy is open-source under the Apache-2.0 license and requires Python~3.12 or later (Fu & Chen, 2026).

MIP Candy is Project Neura’s next-generation infrastructure framework for medical image processing. It defines a handful of common network architectures with their corresponding training, inference, and evaluation pipelines that are out-of-the-box ready to use. Additionally, it also provides integrations with popular frontend dashboards such as Notion, WandB, and TensorBoard.

We provide a flexible and extensible framework for medical image processing researchers to quickly prototype their ideas. MIP Candy takes care of all the rest, so you can focus on only the key experiment designs.