kaggle-nuclei-segmentation

Modified from: https://github.com/neptune-ml/open-solution-data-science-bowl-2018/tree/mask_rcnn_notebook

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.