Head and Neck Tumour Segmentation and Prediction of Patient Survival Project

Overview

Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival

Welcome to the Head and Neck Tumour Segmentation and Prediction of Patient Survival Project!

This project aims to provide methods to automatically segment the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography (CT) images and prediction of progression-free survival in H&N oropharyngeal cancer. We participated to the HEad and neCK TumOR Segmentation and Prediction of Patient Outcome Challenge 2021 (HECKTOR 2021) which creates a platform for comparing segmentation methods and predictions of patient survival.

For the segmentation task, we proposed a new network based on an encoder/decoder architecture with attention mechanisms and full inter- and intra-skip connections and attention mechanisms inspired from UNet3+ to take advantage of low-level and high-level semantics at full scales. Additionally, we used Conditional Random Fields (CRF) as a post-processing step to refine the predicted segmentation maps.

For prediction of patient progression free survival, we extracted relevant clinical, radiomic, and deep learning features using Lasso regression. Our best performing model was a Cox proportional hazard regression.

Installation

  1. Clone the repo
    git clone https://github.com/EmmanuelleB985/Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival.git
  2. Install the packages
    pip install requirements.txt

Usage

  • For segmentation task
   cd src/Segmentation_Task

To train the model

   python main.py
  • For Survival task
   cd src/Survival_Task

To train and evaluate the models, run the notebook Survival.ipynb

License

Distributed under the MIT License. See LICENSE for more information.

OpenMMLab Model Deployment Toolset

Introduction English | 简体中文 MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Major features F

OpenMMLab 1.5k Dec 30, 2022
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)

A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022) https://arxiv.org/abs/2203.09388 Jianqi Ma, Zheto

MA Jianqi, shiki 104 Jan 05, 2023
MonoRCNN is a monocular 3D object detection method for automonous driving

MonoRCNN MonoRCNN is a monocular 3D object detection method for automonous driving, published at ICCV 2021. This project is an implementation of MonoR

87 Dec 27, 2022
This is an easy python software which allows to sort images with faces by gender and after by age.

Gender-age Classifier This is an easy python software which allows to sort images with faces by gender and after by age. Usage First install Deepface

Claudio Ciccarone 6 Sep 17, 2022
Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process

Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process, a complete algorithm library is esta

Fu Pengyou 50 Jan 07, 2023
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
A Python package for faster, safer, and simpler ML processes

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Otovo 6 Dec 13, 2022
Implementation of paper "Graph Condensation for Graph Neural Networks"

GCond A PyTorch implementation of paper "Graph Condensation for Graph Neural Networks" Code will be released soon. Stay tuned :) Abstract We propose a

Wei Jin 66 Dec 04, 2022
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer This is the official pytorch implementation of the CoTr: Paper: CoTr: Ef

218 Dec 25, 2022
Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Cross Transformers - Pytorch (wip) Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch Install $ pip install cross-t

Phil Wang 40 Dec 22, 2022
Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Code for KSDAgg: a KSD aggregated goodness-of-fit test This GitHub repository contains the code for the reproducible experiments presented in our pape

Antonin Schrab 5 Dec 15, 2022
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

RTFM This repo contains the Pytorch implementation of our paper: Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Lear

Yu Tian 242 Jan 08, 2023
CM building dataset Timisoara

CM_building_dataset_Timisoara Date created: Febr-2020 The Timi\c{s}oara Building Dataset - TMBuD - is composed of 160 images with the resolution of 76

Orhei Ciprian 5 Sep 07, 2022
A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

Google Research 258 Dec 29, 2022
Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

12 Nov 11, 2022
Symbolic Music Generation with Diffusion Models

Symbolic Music Generation with Diffusion Models Supplementary code release for our work Symbolic Music Generation with Diffusion Models. Installation

Magenta 119 Jan 07, 2023
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

2 Apr 10, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
[CVPRW 2022] Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Attention Helps CNN See Better: Hybrid Image Quality Assessment Network [CVPRW 2022] Code for Hybrid Image Quality Assessment Network [paper] [code] T

IIGROUP 49 Dec 11, 2022
How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

AdamBNN This is the pytorch implementation of our paper "How Do Adam and Training Strategies Help BNNs Optimization?", published in ICML 2021. In this

Zechun Liu 47 Sep 20, 2022