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Cell Image Classification

Code for training and testing of a set of statistical machine learning models on the 2D HeLa dataset (



usage: [-h] [--dataset DATASET] --space {image,wndchrm,rcdt} --model
               [--transfer-learning] [--SVM-kernel {rbf,linear}] [--preprocessed]

P1 Cell Image Classification

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET
  --space {image,wndchrm,rcdt}
  --model {RF,KNN,SVM,LR,LDA,PLDA,MLP,ShallowCNN,VGG16,InceptionV3}
  --transfer-learning   neural network use pretrained weights instead of
                        training from scratch
  --SVM-kernel {rbf,linear}
  --preprocessed           reproduce the results on Hela dataset reported in the


  • Train A logistic regression model on image space: python --space image --model LR

  • Train A logistic regression model on WND-CHARM feature space: python --space wndchrm --model LR

  • Train InceptionV3 on image space: python --space image --model InceptionV3

  • Train InceptionV3 on image space by fine-tuning a pre-trained model (transfer learning): python --space image --model InceptionV3 --transfer-learning

Reproduce Hela Results

We provide the data used for producing the Hela results as reported in the paper. The preprocessed data is located in data/hela_preprocessed. To reproduce the results, add the --preprocessed flag to commands (this will load the propressed data). For example, to reproduce PLDA classification on wndchrm features result, run python --dataset hela --space wndchrm --model PLDA --preprocessed.

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