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Code repository for the paper entitled "Segmentation-Free Outcome Prediction in Head and Neck Cancer: Deep Learning-based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs) of PET Images" submitted to the European Journal of Nuclear Medicine and Medical Imaging

Jupyter Notebook 36.66% Python 63.34%
deep-learning head-and-neck-cancer machine-learning outcome-prediction pet-ct python survival-analysis transfer-learning

segfreeop's Introduction

Segmentation-Free Outcome Prediction

Official github repository for the paper "Segmentation-Free Outcome Prediction in Head and Neck Cancer: Deep Learning-based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs) of PET Images".

Here is the link to the pre-print Arxiv version: https://arxiv.org/abs/2405.01756

Installation

The code is tested on Windows WSL2 Ubuntu 20.04 and Azure VM Ubuntu 16.04, python 3.8, 3.9, and 3.10

To start, please make a conda environment using the environment.yml file provided:

conda env create -f environment.yml

Acknowledgement

The code is heavily borrowed from this repository.

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