Repository for self-supervised landmark discovery

Overview

self-supervised-landmarks

Repository for self-supervised landmark discovery

Requirements

  • pytorch
  • pynrrd (for 3d images)

Usage

The use of this models is via config files, an example config file for Shepp-logan phantom dataset is given in ./configs/phantom_data.json

To train the model

python train.py --model="2d or 3d" --config_file="path to config file"

The network could be 2d or 3d and the second argument is the config file path. all the other parameters including the save and data director is inside teh config file

For inference

python test.py --model="2d or 3d" --config_file="path to config file" --redu_remove --use_best --num_out=num ts to be retained

redu_remove is a boolean argument that determines if redundant points are removed or not use best is also a boolean argument that determines if the best checkpoint is used or the final checkpoint. num_out is an integer that determines the number of particles to be retained after redundancy removal

Reference

If you are utilizing this code please cite one of the following

  1. Leveraging unsupervised image registration for discovery of landmark shape descriptor
@article{bhalodia2021leveraging,
title = {Leveraging unsupervised image registration for discovery of landmark shape descriptor},
journal = {Medical Image Analysis},
volume = {73},
pages = {102157},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102157},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521002036},
author = {Riddhish Bhalodia and Shireen Elhabian and Ladislav Kavan and Ross Whitaker}
}
  1. Self-supervised discovery of anatomical shape landmarks
@inproceedings{bhalodia2020self,
  title={Self-supervised discovery of anatomical shape landmarks},
  author={Bhalodia, Riddhish and Kavan, Ladislav and Whitaker, Ross T},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={627--638},
  year={2020},
  organization={Springer}
}
Owner
Riddhish Bhalodia
Riddhish Bhalodia
Official implementation of the paper Do pedestrians pay attention? Eye contact detection for autonomous driving

Do pedestrians pay attention? Eye contact detection for autonomous driving Official implementation of the paper Do pedestrians pay attention? Eye cont

VITA lab at EPFL 26 Nov 02, 2022
Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

mqtt-camera-streamer Summary: Publish frames from a connected camera or MJPEG/RTSP stream to an MQTT topic, and view the feed in a browser on another

Robin Cole 183 Dec 16, 2022
The FIRST GANs-based omics-to-omics translation framework

OmiTrans Please also have a look at our multi-omics multi-task DL freamwork 👀 : OmiEmbed The FIRST GANs-based omics-to-omics translation framework Xi

Xiaoyu Zhang 6 Dec 14, 2022
Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

Decoupled Spatial-Temporal Transformer for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, J

51 Dec 13, 2022
Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ

Taosha Fan 47 Nov 15, 2022
Coded illumination for improved lensless imaging

CodedCam Coded Illumination for Improved Lensless Imaging Paper | Supplementary results | Data and Code are available. Coded illumination for improved

Computational Sensing and Information Processing Lab 1 Nov 29, 2021
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut

Meta Research 3.7k Jan 02, 2023
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis Andreas Bl

CompVis Heidelberg 36 Dec 25, 2022
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.

DARP-SBIR Intro This repository contains the source code implementation for ICDM submission paper Deep Reinforced Attention Regression for Partial Ske

2 Jan 09, 2022
CMSC320 - Introduction to Data Science - Fall 2021

CMSC320 - Introduction to Data Science - Fall 2021 Instructors: Elias Jonatan Gonzalez and José Manuel Calderón Trilla Lectures: MW 3:30-4:45 & 5:00-6

Introduction to Data Science 6 Sep 12, 2022
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

Baptiste 53 Nov 16, 2022
Deep Learning for Time Series Forecasting.

nixtlats:Deep Learning for Time Series Forecasting [nikstla] (noun, nahuatl) Period of time. State-of-the-art time series forecasting for pytorch. Nix

Nixtla 5 Dec 06, 2022
This repo provides function call to track multi-objects in videos

Custom Object Tracking Introduction This repo provides function call to track multi-objects in videos with a given trained object detection model and

Jeff Lo 51 Nov 22, 2022
Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

BPR Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR integrates a learning-to-hash techni

Studio Ousia 147 Dec 07, 2022
Code for AutoNL on ImageNet (CVPR2020)

Neural Architecture Search for Lightweight Non-Local Networks This repository contains the code for CVPR 2020 paper Neural Architecture Search for Lig

Yingwei Li 104 Aug 31, 2022
Implementation of RegretNet with Pytorch

Dependencies are Python 3, a recent PyTorch, numpy/scipy, tqdm, future and tensorboard. Plotting with Matplotlib. Implementation of the neural network

Horris zhGu 1 Nov 05, 2021
Official Pytorch Implementation of Unsupervised Image Denoising with Frequency Domain Knowledge

Unsupervised Image Denoising with Frequency Domain Knowledge (BMVC 2021 Oral) : Official Project Page This repository provides the official PyTorch im

Donggon Jang 12 Sep 26, 2022
Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets This is the official implementation of "Towards Good Pract

Sanja Fidler's Lab 52 Nov 22, 2022