[](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d)
# [Self supervised contrastive learning for digital histopathology](https://arxiv.org/abs/2011.13971)
Read the paper on arXiv: [Self supervised contrastive learning for digital histopathology](https://arxiv.org/pdf/2011.13971.pdf)
[](https://github.com/ozanciga/self-supervised-histopathology/releases/tag/tenpercent)
We are sharing a new model trained with more images. This model improves over our original paper and the ImageNet by a large margin! Click on the new model button above to download.
## Pretrained model for self supervised histopathology
You can download the model here with the **.ckpt** extension (The previous models are also accessible, however the newer one performs better on classification, regression and segmentation tasks):
https://github.com/ozanciga/self-supervised-histopathology/releases/tag/tenpercent
We save every 100th epoch, indexed starting from 0.
This checkpoint is epoch 1000, best augmentation settings (see the paper) with Lars optimizer.
Starter code:
```
import torchvision
import torch
MODEL_PATH = '_ckpt_epoch_9.ckpt'
RETURN_PREACTIVATION = False # return features from the model, if false return classification logits
NUM_CLASSES = 4 # only used if RETURN_PREACTIVATION = False
def load_model_weights(model, weights):
model_dict = model.state_dict()
weights = {k: v for k, v in weights.items() if k in model_dict}
if weights == {}:
print('No weight could be loaded..')
model_dict.update(weights)
model.load_state_dict(model_dict)
return model
model = torchvision.models.__dict__['resnet18'](pretrained=False)
state = torch.load(MODEL_PATH, map_location='cuda:0')
state_dict = state['state_dict']
for key in list(state_dict.keys()):
state_dict[key.replace('model.', '').replace('resnet.', '')] = state_dict.pop(key)
model = load_model_weights(model, state_dict)
if RETURN_PREACTIVATION:
model.fc = torch.nn.Sequential()
else:
model.fc = torch.nn.Linear(model.fc.in_features, NUM_CLASSES)
model = model.cuda()
images = torch.rand((10, 3, 224, 224), device='cuda')
out = model(images)
```
# Citation
Please use below to cite this paper if you find this repository useful or if you use pretrained models shared here in your research.
```
@article{SelfSupervisedHisto,
title = {Self supervised contrastive learning for digital histopathology},
journal = {Machine Learning with Applications},
volume = {7},
pages = {100198},
year = {2022},
issn = {2666-8270},
doi = {https://doi.org/10.1016/j.mlwa.2021.100198},
url = {https://www.sciencedirect.com/science/article/pii/S2666827021000992},
author = {Ozan Ciga and Tony Xu and Anne Louise Martel}
}
```
## Sample clustering results
Random sampling
Sampling using K-means clustering of learned features
[![Resnet18 model release](https://img.shields.io/badge/NEW-MODEL-