---
# System prepended metadata

title: team-understand-triage

---

# team-understand-triage

## team zoom room

Peter Steinbach is inviting you to a scheduled Zoom meeting.

Topic: COVID19 challenge team-understand-triage
Time: This is a recurring meeting Meet anytime

Join Zoom Meeting
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Meeting ID: 996 9164 7960
Passcode: 315564
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## a motivation

- on task 1
    - names: Sachin Kumar
    - Peter: for the imputation, I recently saw a nice approach for time series - I am not sure if this is applicable here (one of the authors is known to produce good software, so maybe the paper comes with a usable tool), the link is: https://arxiv.org/abs/2007.01627 (published at Neurips2020) -> code https://github.com/marineLM/NeuMiss
    - Robin Han: establish a baseline knn imputation.  
    - I will have a look   
    - we can also use the published online tool https://aihealthcare.tencent.com/COVID19-Triage_en.html of nature communications paper https://www.nature.com/articles/s41467-020-17280-8 for prediction as another baseline
    
- on task 2: classification of xray images
    - names: Peter
    - nature publication by doctors (mostly US) that critize the advent of ML to classify COVID19 as they describe this endavour as not helpful for the clinic
        - https://www.nature.com/articles/s42256-021-00307-0
    - especially CNN predictions are hard to understand
    - using a library for transfer learning call `timm` https://github.com/rwightman/pytorch-image-models
    - still need to do:
        - compute mean intensity on `normalizedImg`
        - compute standard deviation intensity on `normalizedImg`
        - /home/haicore-project-covchl/cd1710/hida_workspace/HackathonCovidData/trainSet/normalizedImg
            - for all images in this folder
                1. load the image (using `skimage` )
                2. run `np.mean` and `np.std` on the image
                3. store the result in a python list
                4. plot all listed values of `np.mean` and `np.std` in a histogram 
    - currently saw 93% accuracy for first couple of training runs -> need to verify
        - if you like to install timm on Horeka, use this notebook /home/haicore-project-covchl/cd1710/install-timm.ipynb
        - once you have it installed, I ran the classification like so:
```
python ./train.py --model efficientnet_es --pretrained --epochs 30 --aa 'v0' /home/haicore-project-covchl/cd1710/hida_workspace/HackathonCovidData/trainSet/timm_ready --mean 0.5 0.5 0.5 --std 0.2918 0.2918 0.2918
```
    - then I was hoping to run explainability methods, in particular extremal perturbation
        - for this I propose to use: https://github.com/facebookresearch/torchray
        - this library has examples IIRC on extremal 
        
## Zoom meeting at 10.00 am 29 April 2021.

Some notes:
Train sets has two folders:
-trainSet (863 patients)
-testSet(120 patients)
as described in Hackathon documentation.
https://hmgubox2.helmholtz-muenchen.de/index.php/s/mZCrqQcFxJgMcxD?path=%2F

1. Use ‘/testSet/testSet.txt’ as template
2. Rename it with your team’s name
3. Replace the ‘NaN’ with imputed values
(Task 1)
4. Fill-in the ‘Prognosis’ field with either
‘MILD’ or ‘SEVERE’ (Task2)

Uploading solution (deadline 4pm):
1. It would be possible to upload multiple solutions. Only the last submitted solution per team will be taken into account before 4pm
2. Short presentation 3-5 minutes about how a team solved the problem should also be uploaded with a solution 
How to record your pitch: 4min Video - t: https://drive.google.com/file/d/1IXqOo9b3ugCspR7PATqhAsjhixyqIPg3/view

Link to upload your results and short(along with your explanatory video (recommended) or slides): 
https://hmgubox2.helmholtz-muenchen.de/index.php/s/Hz6MKFXFw6nxFo7

Two winners regarding each task solution.
        
    


