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Commit 572b7cc1 authored by lutobi's avatar lutobi
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Update README.md

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......@@ -110,7 +110,7 @@ $ git clone https://lab.compute.dtu.dk/lutobi/mlsp2019_software_package/
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All the contents of the repository can also be downloaded from the GitHub site by using the "Download ZIP" button.
### Run everythinh
### Run everything
In order to run end to end experiment in order to generate all tables and figures from the paper, including the following activities:
* Mask R-CNN part:
* traning 5 separate models
......@@ -237,6 +237,17 @@ Please find documentation [here](documentation.md).
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## Dataset
All analysed models in this thesis are evaluated on the dataset provided by the MICCAI 2015 Gland Segmentation Challenge Contest [article](https://arxiv.org/abs/1603.00275).
The dataset consists of 165 labelled colorectal cancer histological images. 85 images belong to the training set and 80 images are part of the test set, which is divided
into two subsets. Set A contains 60 images, while set B contains 20 images. The training set consists of 37 benign sections and 48 malignant areas. Test A set contains
33 benign sections and 27 malignant areas. Test B set has 4 benign sections and 16 malignant areas. Due to the characteristic of the SA-FCN architecture, authors have prepared
their own version of the labelled images. This is mainly aimed at adding information about the contours of the glands that is necessary to carry out the training of the model.
Figure below shows a few examples of samples from the dataset, with corresponding original labelling and the SA-FCN labelling version.
![example_dataset](/images/example_dataset.PNG)
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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## Step by Step Detection Mask R-CNN
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