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A pre-trained model for Nuclei Classification within Haematoxylin & Eosin stained histology images.
Latest Version
August 15, 2023
50.06 MB

Model Overview

A pre-trained model for classifying nuclei cells as the following types

  • Other
  • Inflammatory
  • Epithelial
  • Spindle-Shaped

This model is trained using DenseNet121 over ConSeP dataset.


The training dataset is from

unzip -q


After downloading this dataset, python script from scripts folder can be used to preprocess and generate the final dataset for training.

python scripts/ --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": [
  "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": [

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
  • Dataset Manager: CacheDataset

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.


4 channels

  • 3 RGB channels
  • 1 signal channel (label mask)


4 channels

  • 0 = Other
  • 1 = Inflammatory
  • 2 = Epithelial
  • 3 = Spindle-Shaped


This model achieves the following F1 score on the validation data provided as part of the dataset:

  • Train F1 score = 0.926
  • Validation F1 score = 0.852

Confusion Metrics for Validation for individual classes are:
Metric Other Inflammatory Epithelial Spindle-Shaped
Precision 0.6909 0.7773 0.9078 0.8478
Recall 0.2754 0.7831 0.9533 0.8514
F1-score 0.3938 0.7802 0.9300 0.8496

Confusion Metrics for Training for individual classes are:
Metric Other Inflammatory Epithelial Spindle-Shaped
Precision 0.8000 0.9076 0.9560 0.9019
Recall 0.6512 0.9028 0.9690 0.8989
F1-score 0.7179 0.9052 0.9625 0.9004
Training Loss and F1

A graph showing the training Loss and F1-score over 100 epochs.

Validation F1

A graph showing the validation F1-score over 100 epochs.

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute training:
python -m monai.bundle run --config_file configs/train.json

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>
Override the train config to execute multi-GPU training:
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"

Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove --standalone, modify --nnodes, or do some other necessary changes according to the machine used. For more details, please refer to pytorch's official tutorial.

Override the train config to execute evaluation with the trained model:
python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
Override the train config and evaluate config to execute multi-GPU evaluation:
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']"
Execute inference:
python -m monai.bundle run --config_file configs/inference.json


[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]


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