This repository provides scripts to reproduce the results in the paper "An annotation-free whole-slide training approach to pathological classification of lung cancer types by deep learning", including model training, inference, visualization, and statistics calculation, etc. Also, the pipeline is seamlessly adaptable to other pathological cases by simply creating new configuration files.
Chen, CL., Chen, CC., Yu, WH. et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat Commun 12, 1193 (2021). https://doi.org/10.1038/s41467-021-21467-y
Chuang, WY., Chen, CC., Yu, WH. et al. Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images. Mod Pathol (2021). https://doi.org/10.1038/s41379-021-00838-2
Copyright (C) 2021 aetherAI Co., Ltd. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
A referenced pre-trained weight for lung cancer type classification is now available at https://drive.google.com/file/d/1XuONWICAzJ-cUKjC7uHLS0YLJhbLRoo1/view?usp=sharing.
The model was trained by TCGA-LUAD and TCGA-LUSC diagnostic slides specified in data_configs/pure_tcga/train_pure_tcga.csv using the config train_configs/pure_tcga/config_pure_tcga_wholeslide_4x.yaml.
Since no normal lung slides were provided in these data sets, the model predicts a slide as either adenocarcinoma (class_id=1) or squamous cell carcinoma (class_id=2).
The prediction scores for normal (class_id=0) should be ignored.
Validation results (n = 192) on data_configs/pure_tcga/val_pure_tcga.csv are listed as follow.
- AUC (LUAD vs LUSC) = 0.9794 (95% CI: 0.9635-0.9953)
- Accuracy (LUAD vs LUSC) = 0.9323 (95% CI: 0.8876-0.9600, @threshold = 0.7 for class1, 0.3 for class2)
Make sure the system contains adequate amount of main memory space (minimal: 256 GB, recommended: 512 GB) to prevent out-of-memory error. For ones who would like to have a try with less concern about model accuracy, setting a lower resizing ratio and image size in configuration can drastically reduce memory consumption, friendly for limited computing resources.
The codes are tested on the environment with Ubuntu 18.04 / CentOS 7.5, Python 3.7.3, cuda 10.0, cudnn 7.6 and Open MPI 4.0.1. Some Python packages should be installed before running the scripts, including
- Tensorflow v1.x (tensorflow-gpu==1.15.3)
- Horovod (horovod==0.19.0)
- MPI for Python (mpi4py==3.0.3)
- OpenSlide 3.4.1 (https://github.com/openslide/openslide/releases/tag/v3.4.1)
- OpenSlide Python (openslide-python=1.1.1)
- Tensorflow Huge Model Support (our package)
- (optional) R 4.0.2 (https://www.r-project.org/)
Refer to requirements.txt for the full list. The installation of these packages should take few minutes.
To initiate a training task, several CSV files, e.g. train.csv, val.csv and test.csv, should be prepared to define training, validation and testing datasets.
These CSV files should follow the format:
[slide_name_1],[class_id_1]
[slide_name_2],[class_id_2]
...
, where [slide_name_*] specify the filename without extension of a slide image and [class_id_*] is an integer indicating a slide-level label (e.g. 0 for normal, 1 for cancerous).
The configuration files for our experiments are placed at data_configs/.
Model hyper-parameters are set up in a YAML file.
For convenience, you can copy one from train_configs/ (e.g. train_configs/config_wholeslide_2x.yaml) and make modifications for your own recipe.
The following table describes each field in a train_config.
| Field | Description |
|---|---|
| RESULT_DIR | Directory to store output stuffs, including model weights, testing results, etc. |
| MODEL_PATH | Path to store the model weight. (default: ${RESULT_DIR}/model.h5) |
| LOAD_MODEL_BEFORE_TRAIN | Whether to load the model weight before training. (default: False) |
| CONFIG_RECORD_PATH | Path to back up this config file. (default: ${RESULT_DIR}/config.yaml) |
| USE_MIXED_PRECISION | Whether to enable mixed precision training. |
| USE_HMS | Whether to enable whole-slide training by optimized unified memory. |
| USE_MIL | Whether to use MIL for training. |
| TRAIN_CSV_PATH | CSV file defining the training dataset. |
| VAL_CSV_PATH | CSV file defining the validation dataset. |
| TEST_CSV_PATH | CSV file defining the testing dataset. |
| SLIDE_DIR | Directory containing all the slide image files (can be soft links). |
| SLIDE_FILE_EXTENSION | File extension. (e.g. ".ndpi", ".svs") |
| SLIDE_READER | Library to read slides. (default: openslide) |
| RESIZE_RATIO | Resize ratio for downsampling slide images. |
| INPUT_SIZE | Size of model inputs in [height, width, channels]. Resized images are padded or cropped to the size. Try decreasing this field when main memory are limited. |
| MODEL | Model architecture to use. One of fixup_resnet50, fixup_resnet34 and resnet34. |
| NUM_CLASSES | Number of classes. |
| BATCH_SIZE | Number of slides processed in each training iteration for each MPI worker. (default: 1) |
| EPOCHS | Maximal number of training epochs. |
| NUM_UPDATES_PER_EPOCH | Number of interations in an epoch. |
| INIT_LEARNING_RATE | Initial learning rate for Adam optimizer. |
| POOL_USE | Global pooling method in ResNet. One of gmp and gap. |
| REDUCE_LR_FACTOR | The learning rate will be decreased by this factor upon no validation loss improvement in consequent epochs. |
| REDUCE_LR_PATIENCE | Number of consequent epochs to reduce learning rate. |
| TIME_RECORD_PATH | Path to store a CSV file recording per-iteration training time. |
| TEST_TIME_RECORD_PATH | Path to store a CSV file recording per-iteration inference time. |
| TEST_RESULT_PATH | Path to store the model predictions after testing in a JSON format. (default: ${RESULT_DIR}/test_result.json) |
| USE_TCGA_VAHADANE | Whether to enable color normalization on TCGA images to TMUH color style. (default: False) |
| ENABLE_VIZ | Whether to draw prediction maps when testing. (default: False) |
| VIZ_SIZE | Size of the output prediction maps in [height, width]. |
| VIZ_FOLDER | Folder to store prediction maps. (default: ${RESULT_DIR}/viz) |
The following fields are valid only when USE_MIL: True.
| Field | Description |
|---|---|
| MIL_PATCH_SIZE | Patch size of the MIL model in [height, width]. |
| MIL_INFER_BATCH_SIZE | Batch size for MIL finding representative patches. |
| MIL_USE_EM | Whether to use EM-MIL. |
| MIL_K | Number of representative patches. (default: 1) |
| MIL_SKIP_WHITE | Whether to skip white patches. (default: True) |
| POST_TRAIN_METHOD | Patch aggregation method to use. One of svm, lr, maxfeat_rf, milrnn and "" (disable). |
| POST_TRAIN_MIL_PATCH_SIZE | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_INIT_LEARNING_RATE | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_REDUCE_LR_FACTOR | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_REDUCE_LR_PATIENCE | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_EPOCHS | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_NUM_UPDATES_PER_EPOCH | (The same as above, for patch aggregation method training process.) |
| POST_TRAIN_MODEL_PATH | Path to store patch aggregation model weights. |
To train a model, simply run
python -m whole_slide_cnn.train --config YOUR_TRAIN_CONFIG.YAML [--continue_mode]
, where --continue_mode is optional that makes the training process begin after loading the model weights.
To enable multi-node, multi-GPU distributed training, simply add mpirun in front of the above command, e.g.
mpirun -np 4 -x CUDA_VISIBLE_DEVICES="0,1,2,3" python -m whole_slide_cnn.train --config YOUR_TRAIN_CONFIG.YAML
Note that you should cd to the root folder of this repo before calling the above commands.
Typically, this step takes days to complete, depending on the computing power, while you can trace the progress in real time from program output.
EM-MIL-SVM, EM-MIL-LR, MIL-RNN and CNN-MaxFeat-based RF involve training a second patch aggregation model, requiring users to run another script to initiate patch aggregation model training. Just like the command above, simply call
[mpirun ...] python -m whole_slide_cnn.post_train --config YOUR_TRAIN_CONFIG.YAML
To evaluate the model or optionally generate prediction heatmap, call
[mpirun ...] python -m whole_slide_cnn.test --config YOUR_TRAIN_CONFIG.YAML
This command will generate a JSON file in the result directory named test_result.json by default.
The file contains the model predictions for each testing slide.
To statistically analyze the results, some scripts are provided in tools/. See the following table for the usage of each tool.
| Tool | Description | Example |
|---|---|---|
| tools/calc_auc.R | Calculate AUC and CI. | tools/calc_auc.R RESULT_DIR/test_result.json |
| tools/compare_auc.R | Testing significance of the AUCs of two models. | tools/compare_auc.R RESULT_DIR_1/test_result.json RESULT_DIR_2/test_result.json |
| tools/draw_roc.py | Draw the ROC diagram. | python tools/draw_roc.py test_result.json:MODEL_NAME:#FF0000 |
| tools/gen_bootstrap_aucs.R | Generate 100 AUCs by bootstrapping. | tools/gen_bootstrap_aucs.R RESULT_DIR/test_result.json |
Note that these tools are currently profiled for lung cancer maintype classification and should be modified when applying to your own tasks.
The slide data from TMUH, WFH and SHH are not publicly available due to patient privacy constraints, but are available uponon reasonable request from the corresponding author Chao-Yuan Yeh or Cheng-Yu Chen. The slide data supporting the cross-site generalization capability in this study are obtained from TCGA via the Genomic Data Commons Data Portal (https://gdc.cancer.gov).
A dataset consists of several slides from TCGA-LUAD and TCGA-LUSC is suitable for testing our pipeline in small scale, with some proper modifications of configuration files described above.