# RED-CNN **Repository Path**: smn568/RED-CNN ## Basic Information - **Project Name**: RED-CNN - **Description**: Pytorch Implementation of Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) - **Primary Language**: Unknown - **License**: MulanPSL-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2025-03-22 - **Last Updated**: 2025-03-22 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # RED_CNN Implementation of [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://arxiv.org/ftp/arxiv/papers/1702/1702.00288.pdf)

There is several things different from the original paper. * The input image patch(64x64 size) is extracted randomly from the 512x512 size image. --> Original : Extract patches at regular intervals from the entire image. * use Adam optimizer ----- ### DATASET The 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge by Mayo Clinic (I can't share this data, you should ask at the URL below if you want) https://www.aapm.org/GrandChallenge/LowDoseCT/ The `data_path` should look like: data_path ├── L067 │ ├── quarter_3mm │ │ ├── L067_QD_3_1.CT.0004.0001 ~ .IMA │ │ ├── L067_QD_3_1.CT.0004.0002 ~ .IMA │ │ └── ... │ └── full_3mm │ ├── L067_FD_3_1.CT.0004.0001 ~ .IMA │ ├── L067_FD_3_1.CT.0004.0002 ~ .IMA │ └── ... ├── L096 │ ├── quarter_3mm │ │ └── ... │ └── full_3mm │ └── ... ... │ └── L506 ├── quarter_3mm │ └── ... └── full_3mm └── ... ------- ## Use Check the arguments. 1. run `python prep.py` to convert 'dicom file' to 'numpy array' 2. run `python main.py --load_mode=0` to training. If the available memory(RAM) is more than 10GB, it is faster to run `--load_mode=1`. 3. run `python main.py --mode='test' --test_iters=100000` to test. ------- ### RESULT