This is the official PyTorch implementation of the paper "SISSI: Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images" to appear in MICCAI 2022 Workshop on Domain Adaptation and Representation Transfer DART 2022.

The code for noisy annotation generation is in noisy_annotations_generation. Specific algorithms have been developed for different state of cells: dead, alive and inhibited, the noisy image level annotations are assumed to be true when developing these algorithms.

You can run deep_learning_code/train_mix.py to train your model in SSSI framework.

• ADELE Adoption for Object detection
• The implementation for determining the optimal point that represents the start of memorisation phase can be found in if_update in deep_learning_code/utils.py.

• We adopt the implementation of TTA and Weighted Boxes Fusion from kentaroy47 for pseudo label generation.
• The code can be found in deep_learning_code/odach.

• The use of seamless clone can be found in seam_less_clone in deep_learning_code/dataloaders/utils.

• The iterative update of synthetic-like images is handled by cell_lab_dataset in deep_learning_code/dataloaders/instance_seg_dataset.py and the flag to perform this update is controlled in correct_labels in deep_learning_code/reference/engine.py.

If this code is useful for your research, please consider citing:

@article{Elbatel2022,
   author = {Marawan Elbatel and Christina Bornberg and Manasi Kattel and Enrique Almar and Claudio Marrocco and Alessandro Bria},
   doi = {10.48550/arxiv.2208.03327},
   month = {8},
   title = {Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images},
   publisher = {arXiv},
   url = {https://arxiv.org/abs/2208.03327v1},
   year = {2022},
}