lin-cell-lab/mmt-psm

[MICCAI2020] Code for paper : Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

MMT-PSM

Code for Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

supplementary

Installation

1. The basecode for this project is built on Detectron-V1. Check INSTALL.md for instructions.
2. We also provide a modified pycocotools file with a new function iouIntUni to compute intersection over union between masks, return iou, intersection, union together. If you want to use this function, follow the install instruction from official pycocotools and replace the file with the same name in the path pycoco.

Abstractions

For more information on some of the main abstractions in detectron-v1 implementation, see ABSTRACTIONS.md.

If you have issues running or compiling this code, we have compiled a list of common issues in TROUBLESHOOTING.md. If your issue is not present there, please feel free to open a new issue.

Dataset Preparation

We provide the code for our dataset class. The annotation style of the dataset is similar to COCO style.

Specifically, a sliding window is used to crop image into 1000*1000 resolution with 0.75 overlapping ratio for training. For eval and test, overlapping ratio is set to 0.

Due to the cropping procedure, there may be some tiny objects in the edge of the image. We skip the tiny object ground truth by self.filter_gt().

Patches without any objects are also removed by self._remove_unsuitable_patches().

Training

To train MMT-PSM

cd MMT-PSM
sh scripts/train_mt.sh

To train without mean teacher framework, you can set DATASETS.NO_LABEL False (do not use unlabeled dataset) or set MT.LAMBDA 0. (weight for consistency loss equals to 0.).

To use Mask R-CNN (ResNet-50 + FPN) as base model (which means to not use IR-Net), set MODEL.RELATION_NMS.USE_RELATION_NMS False and MODEL.RELATION_MASK.USE_RELATION False.

Other hypers:

MT.LAMBDA 5. #weight for consistency losses (l_{psm} and l_{mgd})
MT.FG_HINT 1. # weight for l_{mgd}
MT.CLS_LOSS # weight for l_{psm}
MT.RAMPUP_STEP 250 # a warm up step to gradually increase consistency loss weight using sigmoid-shaped function
MT.RAMPDOWN_STEP 250 # a rampdown step to decrease the weight at the end of training
MT.AUG_K 2 # do k times color augmentation for unlabeled data, if MT.FLIP = True, there will be 4 augmented images with 2 color augs and 2 filpped version for each input.  

Testing

cd MMT-PSM
sh scripts/test.sh
# Hypers:
DATASETS.MODE_IN_TEST 'test' # use 'val' or 'test' to switch the evaluation in val set/test set
MODEL.RELATION_NMS.REG_IOU True # if use IR-Net

Evaluation metrics of AJI and mAP are used. If you use AJI, a modified version of pycocotools is needed (see Installation 2.).

Future Work

This repo may contain some useless hyperparameters. In future we will check again and remove them.

Acknowledgement

Our project is based on code from Detectron (version one).

Citations

Please consider citing the following papers in your publications if they help your research.

@inproceedings{zhou2020deep,
  title={Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation},
  author={Zhou, Yanning and Chen, Hao and Lin, Huangjing and Heng, Pheng-Ann},
 booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},  
 organization={Springer}
  year={2020}
}
@inproceedings{zhou2019irnet,
  title={IRNet: Instance relation network for overlapping cervical cell segmentation},
  author={Zhou, Yanning and Chen, Hao and Xu, Jiaqi and Dou, Qi and Heng, Pheng-Ann},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={640--648},
  year={2019},
  organization={Springer}
}

License

maskrcnn-benchmark is released under the MIT license. See LICENSE for additional details.