Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis

Introduction

This is an implementation of our paper Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis.

Modified from mmclassification.

Support 3D ResNet pre-training with 2D natural-image dataset.

Installation

Please refer to install.md for installation.

Data preparation

Download ImageNet dataset and put as the following structure:

data
  ├──imagenet
        ├── get_meta.sh
        ├── meta
        │   ├── val.txt
        │   ├── test.txt
        │   ├── train.txt
        ├── val
        │   ├──ILSVRC2012_val_00000001.JPEG
        │   ├──ILSVRC2012_val_00000002.JPEG
        │   ├── ...
        ├── test
        │   ├──ILSVRC2012_test_00000001.JPEG
        │   ├──ILSVRC2012_test_00000002.JPEG
        │   ├── ...
        └── train
              └── n10148035
              │    ├── n10148035_10034.JPEG
              │    ├── n10148035_10371.JPEG
              │    ├── ...
              └── n11879895
              │    ├── ...
              └── ...

Pre-train a 3D model on ImageNet dataset

Run this script to pre-train a 3D-ResNet-18 model on ImageNet dataset. It will take around 7 days on 8 Titan XP GPUs.

bash pre_train.sh

Pre-trained Model

We provide models pre-trained on ImageNet dataset which can be used for different 3D medical image analysis tasks.

The pre-trained 3D-ResNet-18 model can be downloaded from Google drive or BaiduYun(verification code: 9hgg).