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Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.
| Date | Stars |
|---|---|
| 2026-07-24 | 294 |
| 2026-07-25 | 294 |
| 2026-07-28 | 294 |
| 2026-07-30 | 294 |
| 2026-08-06 | 294 |
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# MIP Candy: A Candy for Medical Image Processing
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.
:link: [Home](https://mipcandy.projectneura.org)
:link: [Docs](https://mipcandy-docs.projectneura.org)
## Citation
Should you find our work helpful to you, please cite our publication.
```bibtex
@misc{fu2026mipcandymodularpytorch,
title = {MIP Candy: A Modular PyTorch Framework for Medical Image Processing},
author = {Tianhao Fu and Yucheng Chen},
year = {2026},
eprint = {2602.21033},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2602.21033},
}
```
## Installation
Note that MIP Candy requires **Python >= 3.12**.
```shell
pip install "mipcandy[standard]"
```
## Quick Start
Below is an example using the ACDC dataset. The example code replicates most of nnU-Net's features but without
augmentations.
```python
from typing import override
from os.path import exists
from monai.networks.nets import DynUNet
from torch import nn
from torch.utils.data import DataLoader
from mipcandy import SegmentationTrainer, AmbiguousShape, auto_device, download_dataset, NNUNetDataset, inspect, \
load_inspection_annotations, RandomROIDataset
class UNetTrainer(SegmentationTrainer):
@override
def build_network(self, example_shape: AmbiguousShape) -> nn.Module:
kernel_size = [[3, 3, 3]] * 5
strides = [[1, 1, 1]] + [[2, 2, 2]] * 4
return DynUNet(spatial_dims=3, in_channels=example_shape[0], out_channels=self.num_classes,
kernel_size=kernel_size, strides=strides, upsample_kernel_size=strides,
deep_supervision=self.deep_supervision, deep_supr_num=2, res_block=True)
if __name__ == "__main__":
device = auto_device()
download_dataset("nnunet_datasets/ACDC", "tutorial/datasets/ACDC")
dataset = NNUNetDataset("tutorial/datasets/ACDC", align_spacing=True)
if exists("tutorial/datasets/ACDC/annotations.json"):
annotations = load_inspection_annotations("tutorial/datasets/ACDC/annotations.json", dataset)
else:
dataset.device(device=device)
annotations = inspect(dataset)
dataset.device(device="cpu")
annotations.save("tutorial/datasets/ACDC/annotations.json")
dataset = RandomROIDataset(annotations, 2)
train, val = dataset.fold()
train_loader = DataLoader(train, 2, True, num_workers=2, prefetch_factor=2, persistent_workers=True)
val_loader = DataLoader(val, 1, False)
trainer = UNetTrainer("tutorial", train_loader, val_loader, device=device)
trainer.train(1000, note="example with the ACDC dataset")
```Excerpt of 3,482 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4e9bd4afea6bd67b, topic:deep-learning, topic:pytorch
matched fp:4e9bd4afea6bd67b, topic:computer-vision