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MyoQuant-SDH-Model / README.md

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	---
	license: agpl-3.0
	tags:
	- image
	- keras
	- myology
	- biology
	- histology
	- muscle
	- cells
	- fibers
	- myopathy
	- SDH
	- myoquant
	- classification
	- mitochondria
	datasets:
	- corentinm7/MyoQuant-SDH-Data
	metrics:
	- accuracy
	library_name: keras
	model-index:
	- name: MyoQuant-SDH-Resnet50V2
	results:
	- task:
	type: image-classification # Required. Example: automatic-speech-recognition
	name: Image Classification # Optional. Example: Speech Recognition
	dataset:
	type: corentinm7/MyoQuant-SDH-Data # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: MyoQuant SDH Data # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: test # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0.932 # Required. Example: 20.90
	name: Test Accuracy # Optional. Example: Test WER
	---

	## Model description

	<p align="center">
	<img src="https://i.imgur.com/mzALgZL.png" alt="MyoQuant Banner" style="border-radius: 25px;" />
	</p>

	This is the model card for the SDH Model used by the [MyoQuant](https://github.com/lambda-science/MyoQuant) tool.

	## Intended uses & limitations

	It's intended to allow people to use, improve and verify the reproducibility of our MyoQuant tool. The SDH model is used to classify SDH stained muscle fiber with abnormal mitochondria profile.

	## Training and evaluation data

	It's trained on the [corentinm7/MyoQuant-SDH-Data](https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data), avaliable on HuggingFace Dataset Hub.

	## Training procedure

	This model was trained using the ResNet50V2 model architecture in Keras.
	All images have been resized to 256x256 using the `tf.image.resize()` function from Tensorflow.
	Data augmentation was included as layers before ResNet50V2.
	Full model code:

	```python
	data_augmentation = tf.keras.Sequential([
	layers.RandomBrightness(factor=0.2, input_shape=(None, None, 3)), # Not avaliable in tensorflow 2.8
	layers.RandomContrast(factor=0.2),
	layers.RandomFlip("horizontal_and_vertical"),
	layers.RandomRotation(0.3, fill_mode="constant"),
	layers.RandomZoom(.2, .2, fill_mode="constant"),
	layers.RandomTranslation(0.2, .2,fill_mode="constant"),
	layers.Resizing(256, 256, interpolation="bilinear", crop_to_aspect_ratio=True),
	layers.Rescaling(scale=1./127.5, offset=-1), # For [-1, 1] scaling
	])

	# My ResNet50V2
	model = models.Sequential()
	model.add(data_augmentation)
	model.add(
	ResNet50V2(
	include_top=False,
	input_shape=(256,256,3),
	pooling="avg",
	)
	)
	model.add(layers.Flatten())
	model.add(layers.Dense(len(config.SUB_FOLDERS), activation='softmax'))
	```

	```
	_________________________________________________________________
	Layer (type) Output Shape Param #
	=================================================================
	sequential (Sequential) (None, 256, 256, 3) 0

	resnet50v2 (Functional) (None, 2048) 23564800

	flatten (Flatten) (None, 2048) 0

	dense (Dense) (None, 2) 4098

	=================================================================
	Total params: 23,568,898
	Trainable params: 23,523,458
	Non-trainable params: 45,440
	_________________________________________________________________
	```

	We used a ResNet50V2 pre-trained on ImageNet as a starting point and trained the model using an EarlyStopping with a value of 20 (i.e. if validation loss doesn't improve after 20 epoch, stop the training and roll back to the epoch with lowest val loss.)
	Class imbalance was handled by using the class\_-weight attribute during training. It was calculated for each class as `(1/n. elem of the class) * (n. of all training elem / 2)` giving in our case: `{0: 0.6593016912165849, 1: 2.069349315068493}`

	### Training hyperparameters

	The following hyperparameters were used during training:

	- optimizer: Adam
	- Learning Rate Schedule: `ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=1e-7` with START_LR = 1e-5 and MIN_LR = 1e-7
	- Loss Function: SparseCategoricalCrossentropy
	- Metric: Accuracy

	For more details please see the training notebook associated.

	## Training Curve

	Full training results are avaliable on `Weights and Biases` here: [https://api.wandb.ai/links/lambda-science/ka0iw3b6](https://api.wandb.ai/links/lambda-science/ka0iw3b6)
	Plot of the accuracy vs epoch and loss vs epoch for training and validation set.
	![Training Curve](./training_curve.png)

	## Test Results

	Results for accuracy and balanced accuracy metrics on the test split of the [corentinm7/MyoQuant-SDH-Data](https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data) dataset.

	```
	105/105 - 11s - loss: 0.1574 - accuracy: 0.9321 - 11s/epoch - 102ms/step
	Test data results:
	0.9321024417877197
	105/105 [==============================] - 6s 44ms/step
	Test data results:
	0.9166411912436779
	```

	# How to Import the Model

	With Tensorflow 2.10 and over:

	```python
	model_sdh = keras.models.load_model("model.h5")
	```

	With Tensorflow <2.10:
	To import this model RandomBrightness layer had to be added by hand (it was only introduced in Tensorflow 2.10.). So you will need to download the `random_brightness.py` fille in addition to the model.
	Then the model can easily be imported in Tensorflow/Keras using:

	```python
	from .random_brightness import *
	model_sdh = keras.models.load_model(
	"model.h5", custom_objects={"RandomBrightness": RandomBrightness}
	)
	```

	## The Team Behind this Dataset

	The creator, uploader and main maintainer of this model, associated dataset and MyoQuant is:

	- [Corentin Meyer, PhD in Biomedical AI](https://cmeyer.fr) Email: <contact@cmeyer.fr> Github: [@lambda-science](https://github.com/lambda-science)

	Special thanks to the experts that created the data for the dataset and all the time they spend counting cells :

	- Quentin GIRAUD, PhD Student @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <quentin.giraud@igbmc.fr>
	- Charlotte GINESTE, Post-Doc @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <charlotte.gineste@igbmc.fr>

	Last but not least thanks to Bertrand Vernay being at the origin of this project:

	- Bertrand VERNAY, Platform Leader @ [Light Microscopy Facility, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy), 1 rue Laurent Fries, 67404 Illkirch, France <bertrand.vernay@igbmc.fr>

	## Partners

	<p align="center">
	<img src="https://i.imgur.com/m5OGthE.png" alt="Partner Banner" style="border-radius: 25px;" />
	</p>

	MyoQuant-SDH-Model is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte