This is the new multi-stage auto-immunoquantitative analytical model we proposed.
Special thanks to Dr. Cheng @ShenghuaCheng for contributing to this work and WNLO for platform provision.
Taking immunohistochemistry-stained digital cell images as input, the model is merely supervised by positive cell counting labels and transforms whole-image (bag) level counting results into superpixel (instance) level classification results via the specifically designed adaptive top-k instance selection strategy.
- Stage 1: Image-wise regressive positive cell counter
- Stage 2: Superpixel-wise tile instance classifier
- Stage 3: Pixel-wise segmentation encoder-decoder network
Instance classifier provides us semantic information of positive cells. HSV channel separation and thresholding provide us fine-grained profile of positive cells.
Kappa = 0.9319, 4th in Lymphocyte Assessment Hackathon (LYSTO) Challenge. Leaderboard
We also tested our localization method in LYON19.
Visit LYSTO to get data.
- Add image data in
./data
- Train cell counter by
python train_image.py
- Test your counter by
python test_count.py
- Train tile classifier by
python train_tile.py
- Test the classifier and get heatmaps by
python test_tile.py
- Train segmentation network by
python train_seg.py
- Test segmentation network and get masks by
python test_seg.py --draw_masks
- Test segmentation network and get localization points by
python test_seg.py --detect
and use arguments you like. You can find arguments list in the source code file.
... under construction ... stay tuned.
2021-2022 By Newiz