--- tags: - monai - medical library_name: monai license: apache-2.0 --- # Description A pre-trained model for classifying nuclei cells as the following types. - Other - Inflammatory - Epithelial - Spindle-Shaped # Model Overview This model is trained using [DenseNet121](https://docs.monai.io/en/latest/networks.html#densenet121) over [ConSeP](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet) dataset. ## Data The training dataset is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet ```commandline wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip unzip -q consep_dataset.zip ``` ![](images/dataset.jpeg)
## Training configuration The training was performed with the following: - GPU: at least 12GB of GPU memory - Actual Model Input: 4 x 128 x 128 - AMP: True - Optimizer: Adam - Learning Rate: 1e-4 - Loss: torch.nn.CrossEntropyLoss ### Preprocessing After [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip), python script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training. ```commandline python scripts/data_process.py --input /path/to/data/CoNSeP --output /path/to/data/CoNSePNuclei ``` After generating the output files, please modify the `dataset_dir` parameter specified in `configs/train.json` and `configs/inference.json` to reflect the output folder which contains new dataset.json. Class values in dataset are - 1 = other - 2 = inflammatory - 3 = healthy epithelial - 4 = dysplastic/malignant epithelial - 5 = fibroblast - 6 = muscle - 7 = endothelial As part of pre-processing, the following steps are executed. - Crop and Extract each nuclei Image + Label (128x128) based on the centroid given in the dataset. - Combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class. - Update the label index for the target nuclie based on the class value - Other cells which are part of the patch are modified to have label idex = 255 Example `dataset.json` in output folder: ```json { "training": [ { "image": "/workspace/data/CoNSePNuclei/Train/Images/train_1_3_0001.png", "label": "/workspace/data/CoNSePNuclei/Train/Labels/train_1_3_0001.png", "nuclei_id": 1, "mask_value": 3, "centroid": [ 64, 64 ] } ], "validation": [ { "image": "/workspace/data/CoNSePNuclei/Test/Images/test_1_3_0001.png", "label": "/workspace/data/CoNSePNuclei/Test/Labels/test_1_3_0001.png", "nuclei_id": 1, "mask_value": 3, "centroid": [ 64, 64 ] } ] } ``` ## Input and output formats ### Input: 4 channels - 3 RGB channels - 1 signal channel (label mask) ### Output: 4 channels - 0 = Other - 1 = Inflammatory - 2 = Epithelial - 3 = Spindle-Shaped ![](images/val_in_out.jpeg) ## Scores This model achieves the following F1 score on the validation data provided as part of the dataset: - Train F1 score = 0.96 - Validation F1 score = 0.85
Confusion Metrics for Validation for individual classes are (at epoch 50): | Metric | Other | Inflammatory | Epithelial | Spindle-Shaped | |-----------|--------|--------------|------------|----------------| | Precision | 0.5846 | 0.7143 | 0.9158 | 0.8399 | | Recall | 0.2550 | 0.8441 | 0.9193 | 0.8106 | | F1-score | 0.3551 | 0.7738 | 0.9175 | 0.8250 |
Confusion Metrics for Training for individual classes are (at epoch 50): | Metric | Other | Inflammatory | Epithelial | Spindle-Shaped | |-----------|--------|--------------|------------|----------------| | Precision | 0.9059 | 0.9569 | 0.9754 | 0.9494 | | Recall | 0.8370 | 0.9547 | 0.9790 | 0.9502 | | F1-score | 0.8701 | 0.9558 | 0.9772 | 0.9498 | ## Training Performance A graph showing the training Loss and F1-score over 50 epochs. ![](images/train_loss.jpeg)
![](images/train_f1.jpeg)
## Validation Performance A graph showing the validation F1-score over 50 epochs. ![](images/val_f1.jpeg)
## commands example Execute training: ``` python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf ``` Override the `train` config to execute multi-GPU training: ``` torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf ``` Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used. Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details. Override the `train` config to execute evaluation with the trained model: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf ``` Override the `train` config and `evaluate` config to execute multi-GPU evaluation: ``` torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']" --logging_file configs/logging.conf ``` Execute inference: ``` python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf ``` # Disclaimer This is an example, not to be used for diagnostic purposes. # References [1] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019. [[doi](https://doi.org/10.1016/j.media.2019.101563)] # License Copyright (c) MONAI Consortium Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.