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Update README.md

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......@@ -249,43 +249,75 @@ Figure below shows a few examples of samples from the dataset, with correspondin
More informationa about the dataset as well as the scores description can be found on the contest [website](https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/).
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## Expected output
Apart from all output information printed out in the console, you can also check correctness of the experiment by checking the output files. Below we will
list all produced files as well as their examples. Please notice that all output files from this reproducibility package are corresponing to figures and tables from the paper.
*Table 1. Classification (F1 Score) and segmentation (Dice Index) scores using three different post-processing methods. Scores are represented by mean value from five training
followed by the standard error of the mean. *
Table 1 is represented by three separate tables, describing each post-processing methods separately.
![table_1_original](/images/table_1_original.pdf)
![table_1_dcan](/images/table_1_dcan.pdf)
![table_1_our](/images/table_1_our.pdf)
*Table 2. Comparison of the Mask R-CNN and the SA-FCN models’ performance in terms of classification (F1 Score) and segmentation (Dice Index). Scores are represented by mean
value from five training followed by the standard error of the mean.*
Table 2 is represented by two separate tables, describing each model scores separately.
![table2_mask_rcnn](/images/table2_mask_rcnn.pdf)
![table_2_sa_fcn](/images/table_2_sa_fcn.pdf)
*Fig. 4. Visualisation of three post-processing methods on the example of one sample. Images headers describe postprocessing actions applied on sample.*
![figure_4](/images/figure_4.pdf)
*Fig. 5. Visualisation of the contour prediction of the sample, presenting the misalignment problem. The bottom right image show superposed ground truth and prediction
labelling. Yellow colour indicates ground truth pixels not overlapping with prediction, orange indicates prediction pixels not overlapping with the ground truth and
black colour is used to mark properly predicted pixels.*
![figure_5](/images/figure_5.pdf)
*Fig. 6. Visualisation of the same sample prediction before and after post-processing for the Mask R-CNN and SA-FCN models.*
![figure_5](/images/figure_6.pdf)
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## Step by Step Detection Mask R-CNN
In this section we will provide a few tips how to run each part of the experiment separately. We will also provide examples of outputs.
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### Training
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### Predicting
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### Post Processing
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### Scores
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### Outputs
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## Step by Step Detection SA-FCN
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### Training
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### Predicting
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### Post Processing
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### Scores
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### Outputs
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## Authors
<sup>1</sup> Lukasz T. Bienias lutobi@dtu.dk
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