毕业设计 多模态头颈癌识别代码.zip

# colearn A deep learning technique for the spatially varying fusion of spatially-aligned feature maps from multi-modality medical images. ## Dependencies - tensorflow-gpu 1.10+ - CUDA 9.0 - CuDNN 7.0 - python3 - numpy - Pillow This version has only been tested on Ubuntu 16.04 on an NVIDIA GTX 1080 Ti. ## Usage An example architecture is included [here](src/colearn_cnn.py). A main function to use the architecture on PET-CT data is also [included](src/colearn_cnn_petct_main.py). ### Data Preparation The example main file expects PET-CT lung data in the TFRecord format. ```python 'height': tf.FixedLenFeature([], tf.int64) # height of the image 'width': tf.FixedLenFeature([], tf.int64) # width of the image 'depth': tf.FixedLenFeature([], tf.int64) # deprecated will be removed 'ct': tf.FixedLenFeature([], tf.string) # CT image (height * width * 1) 'pt': tf.FixedLenFeature([], tf.string) # PET image (height * width * 1) 'ctlb': tf.FixedLenFeature([], tf.string) # binary masks of anatomical regions (height * width * 2) 'ptlb': tf.FixedLenFeature([], tf.string) # binary mask of tumours (height * w * 1) 'bglb': tf.FixedLenFeature([], tf.string) # binary mask of background (height * width * 1) ``` For the anatomical binary masks, the two channels (depth dimension of 2) are for the lung fields and the mediastinum. ### Training To train using default parameters on a GPU, use: ``` python3 colearn_cnn_petct_main.py \ --mode=train \ --train_data_path=TRAIN_DATA_PATH \ [--valid_data_path=VALID_DATA_PATH] \ --log_root=LOG_ROOT \ --train_dir=LOG_ROOT/TRAIN_DIR \ [--valid_dir=LOG_ROOT/VALID_DIR] ``` where: - `TRAIN_DATA_PATH` is the directory with the \*.tfrecord files for the training data - `VALID_DATA_PATH` is the directory with the \*.tfrecord files for the validation data - `LOG_ROOT` is the directory where the logs for this run will be stored - `TRAIN_DIR` is the subdirectory of `LOG_ROOT` for training logs - `VALID_DIR` is the subdirectory of `LOG_ROOT` for validation logs If `VALID_DATA_PATH` is not specified, there will be no outputs for model validation. If `VALID_DATA_PATH` is specified, then `VALID_DIR` must also be specified. ### Evaluation To evaluate your trained model, use: ``` python3 colearn_cnn_petct_main.py \ --mode=eval \ --eval_data_path=EVAL_DATA_PATH \ --log_root=LOG_ROOT \ --eval_dir=LOG_ROOT/EVAL_DIR \ --checkpoint_to_eval=CKPT_TO_EVAL \ ``` where: - `EVAL_DATA_PATH` is the directory with the \*.tfrecord files for the evaluation data - `LOG_ROOT` is the directory where the logs for this run will be stored - `EVAL_DIR` is the subdirectory of `LOG_ROOT` for evaluation logs - `CKPT_TO_EVAL` is the integer number of the training checkpoint to evaluate ## Citation If you make use of this code in your work, please cite the following paper: `A. Kumar, M. Fulham, D. Feng, and J. Kim, "Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer", arXiv:1810.02492, 2018.` [Link to Paper](https://arxiv.org/abs/1810.02492).