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pathology_nuclei_classification

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metadata
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 over ConSeP dataset.

Data

The training dataset is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet

wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip
unzip -q consep_dataset.zip


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, python script data_process.py from scripts folder can be used to preprocess and generate the final dataset for training.

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:

{
  "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

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.



Validation Performance

A graph showing the validation F1-score over 50 epochs.


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 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]

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.