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+# Learning from ambiguous labels for lung nodule malignancy prediction
+
+This repo contains the official implementation of our paper: Learning from ambiguous labels for lung nodule malignancy prediction, which proposes a multi-view 'divide-and-rule' (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction on chest CT scans. The implementation of DAR model is released.
+<p align="center"><img src="https://raw.githubusercontent.com/Merrical/DAR/master/MVDAR_overview.png" width="90%"></p>
+
+#### [Paper on IEEE](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9705525)
+#### [Paper on arXiv](https://arxiv.org/pdf/2104.11436.pdf)
+
+### Requirements
+This repo was tested with Ubuntu 20.04.4 LTS, Python 3.8, PyTorch 1.9.0, and CUDA 10.1.
+We suggest using virtual env to configure the experimental environment.
+
+1. Clone this repo:
+
+```bash
+git clone https://github.com/Merrical/DAR.git
+```
+
+2. Create experimental environment using virtual env:
+
+```bash
+virtualenv .env --python=3.8 # create
+source .env/bin/activate # activate
+pip install -r requirements.txt
+```
+
+### Bibtex
+```
+@article{liao2022learning,
+  title={Learning from ambiguous labels for lung nodule malignancy prediction},
+  author={Liao, Zehui and Xie, Yutong and Hu, Shishuai and Xia, Yong},
+  journal={IEEE Transactions on Medical Imaging},
+  year={2022},
+  publisher={IEEE}
+}
+```
+
+### Contact Us
+If you have any questions, please contact us ( merrical@mail.nwpu.edu.cn ).
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