🦕HPFG

Official PyTorch implementation of "HPFG: Semi-Supervised Medical Image Segmentation Framework based on Hybrid Pseudo-Labeling and Feature-Guided"

🛠️ Install

If it is a pip environment, run the following command

pip install -r requirements.txt

If it is a Conda environment, run the following command

conda env create -f requirements.yml

✨ DataSets

DataSets	Downloadlink
ACDC	https://www.kaggle.com/datasets/jokerak/acdch5
LIDC	https://www.kaggle.com/datasets/jokerak/lidcidri
ISIC	https://www.kaggle.com/datasets/jokerak/isic2018-224

⭐Train

step 1: Download the code and prepare the running environment

1. Clone this repo to your machine.
2. Make sure Anaconda or Miniconda is installed.
3. Run pip install -r requirement.txt for environment initialization.

step 2: Download Datasets

step 3: It is convenient to perform experiment with HPFG. For example, if you want to run Mean-Teacher algorithm:

1. Modify the config file in config/mean_teacher_unet_30k_224x224_ACDC.yaml as you need

   # Dataset Configuration
   datasets: "acdc" # Dataset name
   num_classes: 4 # Number of categories
   data_path: "/home/ubuntu/data/ACDC" # Dataset placement location
   save_path: "checkpoint/2023-02-26-mean_teacher-ACDC" # Code Save Location
   name: "mean_teacher-ACDC"
   ckpt: None # Pre-training weight position
   cuda: True # Whether to use GPU

2. Run python 2017_03_NIPS_Mean-Teacher_ACDC.py

If you want to run the paper project, please run the following code directly

python main.py

🏷️Support

Currently, we have implemented 7 popular semi supervised medical image segmentation algorithms.

Date	Name	Title	Reference
2017-03	Mean-Teacher	Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results	NeurlPS
2019-07	UAMT	Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation	MICCAI
2021-06	CPS	Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision	CVPR
2021-12	CTCT	Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer	MIDL
2022-02	ICT-MegSeg	AN EMBARRASSINGLY SIMPLE CONSISTENCY REGULARIZATION METHOD FOR SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION	ISBI
2022-03	SSNet	Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation	MICCAI
2022-08	S4CVNet	When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation	CVPR

♥️ Acknowledgement

Our model is related to SSL4MIS. Thanks for their great work!