https://github.com/amirhosein2c/segfreeop
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" published in "Cancers" journal.
https://github.com/amirhosein2c/segfreeop
deep-learning head-and-neck-cancer machine-learning outcome-prediction pet-ct python survival-analysis transfer-learning
Last synced: about 2 months ago
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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" published in "Cancers" journal.
- Host: GitHub
- URL: https://github.com/amirhosein2c/segfreeop
- Owner: Amirhosein2c
- Created: 2024-04-30T08:28:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-17T18:37:37.000Z (about 1 year ago)
- Last Synced: 2025-02-09T04:45:17.335Z (3 months ago)
- Topics: deep-learning, head-and-neck-cancer, machine-learning, outcome-prediction, pet-ct, python, survival-analysis, transfer-learning
- Language: Python
- Homepage:
- Size: 582 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
conda env create -f environment.yml
```
## Acknowledgement
The code is heavily borrowed from [this](https://github.com/cyxie601/ESCC_ML) repository.