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+# AIIJC. The "AI in Medicine" competition track
+
+## Competiton overview
+
+Artifical Intelligence International Junior Contest (AIIJC) has 10 tracks on different themes. \
+This repository contains the code for the "AI in Medicine" track. \
+Specific of competition is that all participants must be under 18 years old.
+
+The main organizer and sponsor of the competition is Sberbank. \
+Organizer and task developer is Alliance for Artificial Intelligence.
+
+## Data
+
+Competition don't provide any data. \
+So we find 3 datasets for training and testing our models:
+
+* [MosMedData](https://mosmed.ai/datasets/covid19_1110/)
+* [MedSeg](http://medicalsegmentation.com/covid19/)
+* [Zenodo](https://zenodo.org/record/3757476#.YRqU0IgzbP_)
+
+## Methods
+
+### Pathology segmentation
+
+For this task we used:
+
+* 2 pretrained Unet++ with backbone EfficientNet-B7 for 2 classes: \
+  COVID-19 ground glass and COVID-19 consolidation.
+* Loss function is IoU
+* Optimizer is Adam with learning rate 1e-4
+
+### Lungs segmentation
+
+Our task need percentage of pathologies per lung. \
+So we need to segment lungs. \
+For this task we prepared specific computer vision pipeline.
+
+1. The mask of pathology was subtracted from the image
+   We did it because pathology prevents
+   us from seeing the lungs (they are totally black, pathology - gray/white).
+2. Find 2 largest contours in the image
+   The contours are the lungs.
+3. Delete image by half and find max overlaps for each contour with half of the image.
+   Here we find the lungs that are on the left and right. (in dicom we have info about the side of the image)
+4. Cut mask of pathology by contours of lungs
+   Our model isn't perfect, so we need to cut the mask of pathology by contours of lungs.
+   This was we reached better accuracy.
+5. Calculate percentage of pathology per lung
+
+Let's see how it works in images:
+...
+
+## Results
+
+![](.images/website.png)
+
+|            image             |      base opencv adaptive thresholding      |         ours adaptive thresholding          |
+|:----------------------------:|:-------------------------------------------:|:-------------------------------------------:|
+| ![](.images/slice_image.png) | ![](.images/base_adaptive_thresholding.png) | ![](.images/ours_adaptive_thresholding.png) |
+
+## Run code
+
+Clone this repository
+```bash
+git clone https://github.com/werserk/AIIJC-med-1st-place.git
+```
+
+Install folder with model checkpoints: [link](https://drive.google.com/file/d/19svztOBB4RhnW7cwuZTDPZb0EiWKdydN/view?usp=sharing)(could be unavailable, contact me)
+
+```bash
+pip install -r requirements.txt
+```
+
+To activate web interface run
+
+```bash
+streamlit run streamlit_app.py
+```
+
+To activate app in bash run
+
+```bash
+python3 bash_app.py --data <image folder> --save_folder <dst folder> --multi --show_legend
+```