docs/README.md

# 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](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
```
![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_dataset.jpeg)<br/>

### 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
      ]
    }
  ]
}
```

## 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.

## Input
4 channels
- 3 RGB channels
- 1 signal channel (label mask)

## Output
4 channels
 - 0 = Other
 - 1 = Inflammatory
 - 2 = Epithelial
 - 3 = Spindle-Shaped

![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_val_in_out.jpeg)

## Performance
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

<hr/>
Confusion Metrics for <b>Validation</b> 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         |


<hr/>
Confusion Metrics for <b>Training</b> 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.

![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_train_loss_v3.png) <br>
![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_train_f1_v3.png) <br>

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

![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_val_f1_v3.png) <br>

#### TensorRT speedup
This bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

| method | torch_tf32(ms) | torch_amp(ms) | trt_tf32(ms) | trt_fp16(ms) | speedup amp | speedup tf32 | speedup fp16 | amp vs fp16|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| model computation | 20.47 | 20.57 | 2.49 | 1.48 | 1.00 | 8.22 | 13.83 | 13.90 |
| end2end | 45 | 49 | 18 | 18 | 0.92 | 2.50 | 2.50 | 2.72 |

Where:
- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
- `end2end` means run the bundle end-to-end with the TensorRT based model.
- `torch_tf32` and `torch_amp` are for the PyTorch models with or without `amp` mode.
- `trt_tf32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.
- `speedup amp`, `speedup tf32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model
- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:
 - TensorRT: 10.3.0+cuda12.6
 - Torch-TensorRT Version: 2.4.0
 - CPU Architecture: x86-64
 - OS: ubuntu 20.04
 - Python version:3.10.12
 - CUDA version: 12.6
 - GPU models and configuration: A100 80G

## 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](https://docs.monai.io/en/latest/config_syntax.html).

#### 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](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).

#### 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
```

#### Execute inference with the TensorRT model:

```
python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"
```

# 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.