metadata
license: cc-by-nc-sa-4.0
language:
- en
pipeline_tag: image-segmentation
library_name: keras
tags:
- histology
- pathology
- vision
- kidney
- transplantation
- Masson's trichrome
Model Card for EffNetV2S_DLV3_512_interstitial_fibrosis_kidney_transplant
Requesting Access
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Model Description
- Developed by: Bertrand Chauveau, Bordeaux University Hospital
- Model type: Keras segmentation model, DeepLabv3+ architecture with an EfficientNetV2S backbone, channels last
- Task: semantic segmentation of interstitial fibrosis in kidney transplant biopsies
- Dataset: Trained on green Masson's trichrome, RGB tile 512 x 512 pixels at a resolution around 1 μm/pixel
- Input: shape (batch_size, 512, 512, 3), raw pixel values 0.-255. as float32
- Repository: https://github.com/bertrandchauveau/FIAT_DL
- Paper: pending
- License: cc-by-nc-sa-4.0
Intended Use & Limitations
Intended Use
This model is intended for automated segmentation of fibrosis in kidney transplant histopathology slides for research purposes.
Limitations
- Research use only
- Stain specificity: The model was trained specifically on green Masson's trichrome slides from Bordeaux University Hospital
How To Use
Load model
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" or "tensorflow"
import keras
from huggingface_hub import login
login() # login with your User Access Token, found at https://huggingface.co/settings/tokens
#load model
model = keras.saving.load_model("hf://bertrandchauveau/EffNetV2S_DLV3_512_interstitial_fibrosis_kidney_transplant")
Inference
from keras.utils import img_to_array, load_img
import numpy as np
img_size = (512, 512)
threshold = 0.35 #recommended threshold value
#get an example image
!wget https://raw.githubusercontent.com/bertrandchauveau/FIAT_DL/main/example_tile.jpg
#image to array
img_array = img_to_array(load_img('example_tile.jpg', target_size=img_size))
img_batch = np.expand_dims(img_array, axis=0) #expects shape (batch, 512, 512, 3) and float32 values 0-255
#prediction
pred = model.predict(img_batch)[0] #shape (1,512,512,2) to (512,512,2)
mask = np.where(pred[...,1] >= threshold, 255, 0).astype(np.uint8) #pred[...,1] is the score of fibrosis, 0 of "non-fibrosis"
Software Dependencies
Python Packages
- keras>3.0
Contact
For any additional questions or comments, contact Bertrand Chauveau (bertrand.chauveau@chu-bordeaux.fr)
library_name: keras
This model has been uploaded using the Keras library and can be used with JAX, TensorFlow, and PyTorch backends.
For more details about the model architecture, check out config.json.