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kaggle-nuclei-segmentation

Software Environment:

  • Python:
  • Tensorflow: 1.7.0
  • Pandas: 0.22.0
  • Numpy: 1.14.2
  • tqdm: 4.20.0
  • keras: 2.1.5
  • scipy: 1.0.1
  • skimage: 0.13.1
  • imgaug: 0.2.5

Hardware Environment:

  • Google Cloud Platform:
  • CPU: 8 core, 32GB RAM.
  • GPU: Nvidia K80, 11G.
  • 200G HDD.

How to run & repeat submission:

Basic Downloads: (Link: https://goo.gl/9Xjfuv)

  • stage 1 train\test data. (For model training repeat)
  • External data link (already in the provided stage 1 train data).
  • Link: https://www.kaggle.com/voglinio/bowl2018-external
  • stage1_metadata.csv. (For model training repeat)
  • stage1_background_foreground_classes.csv. (For model training repeat)
  • Model trained weights. (For submissions repeat)
  • stage2_metadata.csv. (For submissions repeat)
  • stage2_backgroud_foreground_classes.csv. (For submissions repeat)
  • Stage 2 test data downloads: https://www.kaggle.com/c/data-science-bowl-2018/data

Training:

  • Run the following jupyter notebooks for two models: (Using different pre-processing)
    • training.ipynb
  • Modifies:
    • 'paths' in the notebook to save the model weights trained.

Generate Submissions:

  • Run "post process-stage2.ipynb" for two models’ predictions:
  • Modifies:
    • 'paths' in the notebook to the model weights to be used in inference.
    • TEST_PATH to stage 2 test data
    • META_DATA_PATH to stage2_metadata.csv
    • BG_DATA_PATH to stage2_backgroud_foreground_classes.csv
  • Run Ensemble-stage2.ipynb to combine two models’ predictions.

Solution Description:

  • Mask R-CNN: Follow https://github.com/matterport/Mask_RCNN
  • Post-processing (Most time spent here):
    • Soft-nms
    • Flexible soft-nms
    • Parallel Processing to speed up
  • Augmentation.
    • np.fliplr, np.flpud, np.rot90
    • Random crop for external dataset.
      • Too many instances in one original image.
  • Testing-Time Augmentation.
    • Rotation, even np.rot90 hurt performance. I used np.fliplr\ud only.
  • Background-Foreground Processing:
    • Training different model based on different background-foreground type.
    • Also applied with different preprocessing.
  • IOU Calculation.
    • Pixel level instead of using box.
    • For situation when nuclei (say A) is long and inclined, s.t. the bounding box has very high 'box' iou with some other smaller nuclei around nuclei A.
  • Implementation: (Calculating pixel IOU between A and B):
    • Binarize masks of A and B.
    • Pixel >= .5 to be 1, else 0
    • Returns (np.logical_and(A, B).sum() / min(A.sum(), B.sum())
    • This improves a lot, since the following case performs much better:
    • Small nuclei totally overlapped with large nuclei
  • Misc.
    • Compile fixed labels\masks.
    • Hyper-parameter tuning: detection min confidence, max detected instances…
    • Filter invalid\small boxes out of training phase.
    • Train longer. (20 -> 90 epochs).
    • Schedule learning rate.    * Compile external data set. (H&E stained dataset)
    • Tried deform conv layer.
      • But doesn’t work out well.

Possible Improvements:

  • More external datasets to generalize to different kind of nuclei.
  • Using Crop to train instead of resize => Better at dealing with small nuclei.
  • Try Modified IOU calculation in training phase as well.
  • Try UNet and Ensemble.