mipcandy 1.1.0b0

A Candy for Medical Image Processing

MIP Candy: A Candy for Medical Image Processing

MIP Candy is Project Neura's next-generation infrastructure framework for medical image processing. It integrates a handful number 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 adapters to popular frontend dashboards such as Notion, WandB, and TensorBoard.

:link: Home

:link: Docs

Key Features

Why MIP Candy? :thinking:

Easy adaptation to fit your needs

We provide tons of easy-to-use techniques for training that seamlessly support your customized experiments.

• Sliding window
• ROI inspection
• ROI cropping to align dataset shape (100% or 33% foreground)
• Automatic padding
• ...

You only need to override one method to create a trainer for your network architecture.

from typing import override

from torch import nn
from mipcandy import SegmentationTrainer


class MyTrainer(SegmentationTrainer):
    @override
    def build_network(self, example_shape: tuple[int, ...]) -> nn.Module:
        ...

Satisfying command-line UI design

Built-in 2D and 3D visualization for intuitive understanding

High availability with interruption tolerance

Interrupted experiments can be resumed with ease.

Support of various frontend platforms for remote monitoring

MIP Candy Supports Notion, WandB, and TensorBoard.

Installation

Note that MIP Candy requires Python >= 3.12.

pip install "mipcandy[standard]"

Quick Start

Below is a simple example of a nnU-Net style training. The batch size is set to 1 due to the varying shape of the dataset.

from typing import override

import torch
from mipcandy_bundles.unet import UNetTrainer
from torch.utils.data import DataLoader

from mipcandy import download_dataset, NNUNetDataset


class PH2(NNUNetDataset):
    @override
    def load(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        image, label = super().load(idx)
        return image.squeeze(0).permute(2, 0, 1), label


download_dataset("nnunet_datasets/PH2", "tutorial/datasets/PH2")
dataset, val_dataset = PH2("tutorial/datasets/PH2", device="cuda").fold()
dataloader = DataLoader(dataset, 1, shuffle=True)
val_dataloader = DataLoader(val_dataset, 1, shuffle=False)
trainer = UNetTrainer("tutorial", dataloader, val_dataloader, device="cuda")
trainer.train(1000, note="a nnU-Net style example")