This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

Related tags

Deep Learninggenerative-adversarial-networksemi-supervised-learningdiabetic-retinopathy-detectiondisease-predictionfundus-image-analysisfluorescein-angiographyvision-transformervision-transformers
Overview

ICCV Workshop 2021 VTGAN

PWC

This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers" which is part of the supplementary materials for ICCV 2021 Workshop on Computer Vision for Automated Medical Diagnosis. The paper has since been accpeted and presented at ICCV 2021 Workshop.

Arxiv Pre-print

https://arxiv.org/abs/2104.06757

CVF ICCVW 2021

https://openaccess.thecvf.com/content/ICCV2021W/CVAMD/html/Kamran_VTGAN_Semi-Supervised_Retinal_Image_Synthesis_and_Disease_Prediction_Using_Vision_ICCVW_2021_paper.html

IEE Xplore ICCVW 2021

https://ieeexplore.ieee.org/document/9607858

Citation

@INPROCEEDINGS{9607858,
  author={Kamran, Sharif Amit and Hossain, Khondker Fariha and Tavakkoli, Alireza and Zuckerbrod, Stewart Lee and Baker, Salah A.},
  booktitle={2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, 
  title={VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers}, 
  year={2021},
  volume={},
  number={},
  pages={3228-3238},
  doi={10.1109/ICCVW54120.2021.00362}
}

Pre-requisite

  • Ubuntu 18.04 / Windows 7 or later
  • NVIDIA Graphics card

Installation Instruction for Ubuntu

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.7
  • Install Tensorflow-Gpu version-2.5.0 and Keras version-2.5.0
sudo pip3 install tensorflow-gpu
sudo pip3 install keras
  • Install packages from requirements.txt
sudo pip3 install -r requirements.txt

Dataset download link for Hajeb et al.

https://sites.google.com/site/hosseinrabbanikhorasgani/datasets-1/fundus-fluorescein-angiogram-photographs--colour-fundus-images-of-diabetic-patients
  • Please cite the paper if you use their data
@article{hajeb2012diabetic,
  title={Diabetic retinopathy grading by digital curvelet transform},
  author={Hajeb Mohammad Alipour, Shirin and Rabbani, Hossein and Akhlaghi, Mohammad Reza},
  journal={Computational and mathematical methods in medicine},
  volume={2012},
  year={2012},
  publisher={Hindawi}
}
  • Folder structure for data-preprocessing given below. Please make sure it matches with your local repository.
├── Dataset
|   ├──ABNORMAL
|   ├──NORMAL

Dataset Pre-processing

  • Type this in terminal to run the random_crop.py file
python3 random_crop.py --output_dir=data --input_dim=512 --datadir=Dataset
  • There are different flags to choose from. Not all of them are mandatory.
    '--input_dim', type=int, default=512
    '--n_crops', type=int, default=50
    '--datadir', type=str, required=True, help='path/to/data_directory',default='Dataset'
    '--output_dir', type=str, default='data'

NPZ file conversion

  • Convert all the images to npz format
python3 convert_npz.py --outfile_name=vtgan --input_dim=512 --datadir=data --n_crops=50
  • There are different flags to choose from. Not all of them are mandatory.
    '--input_dim', type=int, default=512
    '--n_crops', type=int, default=50
    '--datadir', type=str, required=True, help='path/to/data_directory',default='data'
    '--outfile_name', type=str, default='vtgan'
    '--n_images', type=int, default=17

Training

  • Type this in terminal to run the train.py file
python3 train.py --npz_file=vtgan --batch=2 --epochs=100 --savedir=VTGAN
  • There are different flags to choose from. Not all of them are mandatory
    '--epochs', type=int, default=100
    '--batch_size', type=int, default=2
    '--npz_file', type=str, default='vtgan', help='path/to/npz/file'
    '--input_dim', type=int, default=512
    '--n_patch', type=int, default=64
    '--savedir', type=str, required=False, help='path/to/save_directory',default='VTGAN'
    '--resume_training', type=str, required=False,  default='no', choices=['yes','no']

License

The code is released under the BSD 3-Clause License, you can read the license file included in the repository for details.

Owner
Sharif Amit Kamran
Interested in Deep learning for Medical Imaging and Computer Vision. Designing robust generative architectures for Ophthalmology and Calcium Imaging.
Sharif Amit Kamran
GitHub Repository
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows

Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono

62 Nov 23, 2022
This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

DeepShift This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper, that aims to replace multiplicati

Mostafa Elhoushi 88 Dec 23, 2022
Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on [ Paper ] [ Project Page ] This repository contains the code fo

Andrew Jong 97 Dec 13, 2022
CowHerd is a partially-observed reinforcement learning environment

CowHerd is a partially-observed reinforcement learning environment, where the player walks around an area and is rewarded for milking cows. The cows try to escape and the player can place fences to h

Danijar Hafner 6 Mar 06, 2022
Time-Optimal Planning for Quadrotor Waypoint Flight

Time-Optimal Planning for Quadrotor Waypoint Flight This is an example implementation of the paper "Time-Optimal Planning for Quadrotor Waypoint Fligh

Robotics and Perception Group 38 Dec 02, 2022
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng Internati

Princeton Vision & Learning Lab 115 Jan 04, 2023
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
This tool uses Deep Learning to help you draw and write with your hand and webcam.

This tool uses Deep Learning to help you draw and write with your hand and webcam. A Deep Learning model is used to try to predict whether you want to have 'pencil up' or 'pencil down'.

lmagne 169 Dec 10, 2022
An efficient framework for reinforcement learning.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

16 Nov 30, 2022
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
A collection of models for image<->text generation in ACM MM 2021.

Bi-directional Image and Text Generation UMT-BITG (image & text generator) Unifying Multimodal Transformer for Bi-directional Image and Text Generatio

Multimedia Research 63 Oct 30, 2022
Spherical CNNs

Spherical CNNs Equivariant CNNs for the sphere and SO(3) implemented in PyTorch Overview This library contains a PyTorch implementation of the rotatio

Jonas Köhler 893 Dec 28, 2022
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation

Auto-Seg-Loss By Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai This is the official implementation of the ICLR 2021 paper Auto

61 Dec 21, 2022
LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.

LowRankModels.jl LowRankModels.jl is a Julia package for modeling and fitting generalized low rank models (GLRMs). GLRMs model a data array by a low r

Madeleine Udell 183 Dec 17, 2022
This repo contains the code and data used in the paper "Wizard of Search Engine: Access to Information Through Conversations with Search Engines"

Wizard of Search Engine: Access to Information Through Conversations with Search Engines by Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zh

19 Oct 27, 2022
Implementation of Axial attention - attending to multi-dimensional data efficiently

Axial Attention Implementation of Axial attention in Pytorch. A simple but powerful technique to attend to multi-dimensional data efficiently. It has

Phil Wang 250 Dec 25, 2022
A Traffic Sign Recognition Project which can help the driver recognise the signs via text as well as audio. Can be used at Night also.

Traffic-Sign-Recognition In this report, we propose a Convolutional Neural Network(CNN) for traffic sign classification that achieves outstanding perf

Mini Project 64 Nov 19, 2022
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

Brad 24 Nov 11, 2022
[IEEE TPAMI21] MobileSal: Extremely Efficient RGB-D Salient Object Detection [PyTorch & Jittor]

MobileSal IEEE TPAMI 2021: MobileSal: Extremely Efficient RGB-D Salient Object Detection This repository contains full training & testing code, and pr

Yu-Huan Wu 52 Jan 06, 2023