Skip to content

Latest commit

 

History

History
56 lines (45 loc) · 3.96 KB

readme.md

File metadata and controls

56 lines (45 loc) · 3.96 KB

Kaggle Prostate cANcer graDe Assessment (PANDA) Challenge

Here is the solution code of Team ChienYiChi for https://www.kaggle.com/c/prostate-cancer-grade-assessment/overview.

Dependencies

pip install -r requirements.txt

Experiments Record

tile images model

type model private kappa public kappa local kappa karolinska kappa radboud kappa fold num image size num tiles epoch TTA
cls tiles-resnext50-netvlad 0.896 0.879 0.8602 0.8884 0.8089 0 256 20 27 8
cls tiles-eb0-netvlad 0.882 0.849 0.8762 0.877 0.851 0 256 20 26 8
cls tiles-eb0-netvlad 0.857 0.856 0.8834 0.8714 0.8692 0 256 36 22 8
cls tiles-resnet34-netvlad 0.87 0.848 0.8745 0.8697 0.8522 0 256 20 28 8
reg tiles-eb0-netvlad 0.899 0.859 0.8777 0.8820 0.8470 0 256 20 29 8
reg tiles-eb0-netvlad 0.920 0.881 0.8952 0.8976 0.8704 0 256 36 28 8
reg tiles-eb0-netvlad 0.903 0.893 0.886 0.8979 0.8464 1 256 36 22 8
reg tiles-eb4-netvlad train with BRS（blue ratio selection）,test without BRS 0.909 0.90 0.8826 0.9047 0.8335 1 256 36 26 8
reg tiles-eb4-netvlad test with BRS 0.913 0.896 0.8826 0.9047 0.8335 1 256 36 26 8
reg tiles-eb0-netvlad with attention model(128 tiles) to select tiles 0.899 0.88 0.8833 0.9034 0.8367 1 256 16 27 8 TTA only for score model
reg tiles-eb0-netvlad with attention model to select tiles 0.910 0.88 0.8833 0.9034 0.8367 1 256 16 27 8 TTA only for score model
reg tiles-eb0-netvlad with attention model to select tiles 0.813 0.874 0.8859 0.8945 0.8481 1 256 36 25 8 TTA only for score model
reg tiles-eb4-netvlad with attention model to select tiles 0.908 0.897 0.8766 0.9035 0.8246 1 256 16 27 8 TTA only for score model
reg newcv tiles-eb4-netvlad with attention model to select tiles 0.904 0.899 0.8812 0.8958 0.8437 1 256 16 27 8 TTA only for score model
ord reg newcv tiles-eb0-netvlad with attention model to select tiles 0.913 0.884 0.8958 0.8972 0.8732 1 256 16 27 8 TTA only for score model
ord reg newcv tiles-eb4-netvlad with attention model to select tiles 0.900 0.879 0.887 0.9006 0.8524 1 256 16 27 8 TTA only for score model
reg stitch-tiles-regnety_800m with attention model to select tiles 0.911 0.893 0.8935 0.8872 0.8757 1 256 16 28 8 TTA only for score model

How to Run

  • generate tiles using preprocess.py

train one-stage model

  1. set model type and hyperparameters in config.py
  2. change model function in train.py

train two-stage model (attention model + score model)

  1. set model type and hyperparameters in config.py
  2. change the model function to the efficienet model with attention layer in train.py
  3. generate tiles weights using generate_weights.py which will output a tiles weights csv file
  4. set model type and hyperparameter again in config.py if you want to change the model type , for example regression or ordinal regression
  5. change the model function in train.py ,for example, efficientnet with NetVlad layer

References

Thanks everyone who shared their ideas on Kaggle discussion, I learned a lot from them.