Retinal vessel segmentation based on GT-UNet

Related tags

Deep LearningGT-U-Net
Overview

Retinal vessel segmentation based on GT-UNet

Introduction

This project is a retinal blood vessel segmentation code based on UNet-like Group Transformer Network (GT-UNet), including data preprocessing, model training and testing, visualization, etc.

Requirements

The main package and version of the python environment are as follows

# Name                    Version         
python                    3.7.9                    
pytorch                   1.7.0         
torchvision               0.8.0         
cudatoolkit               10.2.89       
cudnn                     7.6.5           
matplotlib                3.3.2              
numpy                     1.19.2        
opencv                    3.4.2         
pandas                    1.1.3        
pillow                    8.0.1         
scikit-learn              0.23.2          
scipy                     1.5.2           
tensorboardX              2.1        
tqdm                      4.54.1             

Usage

The project structure and intention are as follows :

VesselSeg-Pytorch			# Source code		
    ├── config.py		 	# Configuration information
    ├── lib			            # Function library
    │   ├── common.py
    │   ├── dataset.py		        # Dataset class to load training data
    │   ├── datasetV2.py		        # Dataset class to load training data with lower memory
    │   ├── extract_patches.py		# Extract training and test samples
    │   ├── help_functions.py		# 
    │   ├── __init__.py
    │   ├── logger.py 		        # To create log
    │   ├── losses
    │   ├── metrics.py		        # Evaluation metrics
    │   └── pre_processing.py		# Data preprocessing
    ├── models		        # All models are created in this folder
    │   ├── __init__.py
    │   ├── nn
    │   └── GT-UNet.py
    ├── prepare_dataset	        # Prepare the dataset (organize the image path of the dataset)
    │   ├── chasedb1.py
    │   ├── data_path_list		  # image path of dataset
    │   ├── drive.py
    │   └── stare.py
    ├── tools			     # some tools
    │   ├── ablation_plot.py
    │   ├── ablation_plot_with_detail.py
    │   ├── merge_k-flod_plot.py
    │   └── visualization
    ├── function.py			        # Creating dataloader, training and validation functions 
    ├── test.py			            # Test file
    └── train.py			          # Train file

Training model

Please confirm the configuration information in the config.py. Pay special attention to the train_data_path_list and test_data_path_list. Then, running:

python train.py

You can configure the training information in config, or modify the configuration parameters using the command line. The training results will be saved to the corresponding directory(save name) in the experiments folder.

3) Testing model

The test process also needs to specify parameters in config.py. You can also modify the parameters through the command line, running:

python test.py  

The above command loads the best_model.pth in ./experiments/GT-UNet_vessel_seg and performs a performance test on the testset, and its test results are saved in the same folder.

Owner
Kent0n
Kent0n
A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.

WebDataset WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives and us

1.1k Jan 08, 2023
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

Graph Notebook: easily query and visualize graphs The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Us

Amazon Web Services 501 Dec 28, 2022
A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

Youzhi Gu 7 Nov 27, 2021
Algorithmic Trading using RNN

Deep-Trading This an implementation adapted from Rachnog Neural networks for algorithmic trading. Part One — Simple time series forecasting and this c

Hazem Nomer 29 Sep 04, 2022
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

730 Jan 09, 2023
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
Repository for the paper titled: "When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer"

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer This repository contains code for our paper titled "When is BERT M

Princeton Natural Language Processing 9 Dec 23, 2022
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
The backbone CSPDarkNet of YOLOX.

YOLOX-Backbone The backbone CSPDarkNet of YOLOX. In this project, you can enjoy: CSPDarkNet-S CSPDarkNet-M CSPDarkNet-L CSPDarkNet-X CSPDarkNet-Tiny C

Jianhua Yang 9 Aug 22, 2022
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want

Funny_muscle_enhancer :) 1.Discription: This is just a funny project that we want to see AutoEncoder (AE) can actually work on the some features. We w

Jing-Yao Chen (Jacob) 8 Oct 01, 2022
small collection of functions for neural networks

neurobiba other languages: RU small collection of functions for neural networks. very easy to use! Installation: pip install neurobiba See examples h

4 Aug 23, 2021
Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Learning Causal Semantic Representation for Out-of-Distribution Prediction This repository is the official implementation of "Learning Causal Semantic

Chang Liu 54 Dec 01, 2022
CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view.

CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view. Center-based 3D Object Detection and Tracking, Tianwei Yin, Xin

Tianwei Yin 134 Dec 23, 2022
Rasterize with the least efforts for researchers.

utils3d Rasterize and do image-based 3D transforms with the least efforts for researchers. Based on numpy and OpenGL. It could be helpful when you wan

Ruicheng Wang 8 Dec 15, 2022
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Semi-supervised Deep Kernel Learning This is the code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data

58 Oct 26, 2022
MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

Front-end View Backend View Table of Contents Description Prerequisites Running Basic Information Measurements User Interface Feedback and usage Descr

Center for Computational Imaging and Personalized Diagnostics 58 Dec 02, 2022
Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"

StyleAttack Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer" Prepare Pois

THUNLP 19 Nov 20, 2022