This repository provides the Pytorch implementation of the paper AE-CFE: Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.
Our unsupervised feature extraction approach starts with a Mask R-CNN model which is trained to detect single white blood cells in scanned patient's blood smears. The autoencoder uses for every detected cell instance-specific features extracted to train. The autoencoder uses the instance features as input and tries to reconstruct (i) instance features and (ii) single-cell images.
We use three different white blood cells datasets to evaluate our method:
- The Matek-19 dataset consists of over 18,000 annotated white blood cells from 100 acute myeloid leukameia patients. It is published by Matek et al. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks.
- The INT-20 in-house dataset has around 42,000 images coming from 18 different classes.
- The Acevedo-20 dataset has over 17,000 images which is published by Acevedo et al. A dataset of microscopic peripheral blood cell images for the development of automatic recognition systems.
To train the model, first, you need to extract the features from the Mask-RCNN model, please run FeatureExtraction.py in Mask-RCNN folder, and please download the trained Mask-RCNN model from here and make a logs folder in Mask-RCNN and put it in that directory:
https://drive.google.com/file/d/1KP07ViFmT_zAuR2Ga2kAnD5V2j10zHVW/view?usp=sharing
Secondly, to run AE code, please run train.py, then to extract the features, you can use FeatureExtraction.py code and finally to evaluate the quantitatively of the extracted features by AE-CFE, please run RandomForest.py.
I used tensorflow 1 and it worked good, there are ways to get this working on newer tensorflow versions, please consult https://github.com/matterport/Mask_RCNN
If you use this code, please cite
@inproceedings{salehi2022unsupervised,
title={Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification},
author={Salehi, Raheleh and Sadafi, Ario and Gruber, Armin and Lienemann, Peter and Navab, Nassir and Albarqouni, Shadi and Marr, Carsten},
booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022, Proceedings, Part III},
pages={739--748},
year={2022},
organization={Springer}
}