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# System prepended metadata

title: Satya's Research Project

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# Satya's Research Project

## Motivation

- HZDR has devices that produce large datasets
    - as an example ROFEX tomogram: `256x256x150k` at `uint16`
    - segmentation is the task of finding the probability that a given pixel inside an image/tensor belongs to a given class of objects
    - an introduction to (semantic) segmentation see https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
    - typical goal at HZDR: instance segmentation
    - challenge: use segmentation ML networks on large datasets
        - typical approach: tile the data -> depends on how large your objects
- the literature has offerend an alternative: https://paperswithcode.com/paper/iunets-fully-invertible-u-nets-with-learnable
    - in 2015, a deep learning architecture called Unet by Ronneberger et al http://arxiv.org/abs/1505.04597
    - converted Unet to an invertable network -> the authors claim that they reduce the memory footprint

## Research Question

Disclaimer: these research questions warrant work that amounts to likely more than 15 weeks FTE 

1) reproduce the results of the iUnet paper (*requirement*)
    a) measure the memory footprint of a Unet on 3D data
    b) measure the memory footprint of a iUnet on 3D data

2) what is the performance difference (with respect to prediction quality) imposed by a iUnet with respect to 
    a) variance of prediction with respect to the DICE score (*requirement*)
    b) uncertainty of prediction for a given pixel (*super-duper*)

3) (optional) write a reproduction paper -> for more details, see https://rescience.github.io/write/

## How to interact

- meet every week for 30 minutes for quick updates
- meet every 3 weeks for larger sessions of about 1h

## computational resources

1) try to get you account on `taurus`
2) if that fails, try to get you an account on `herema`

# Homework

- have a closer look at backpropagation, use Sebastian Raschka's lecture videos
    - https://youtu.be/1nqCZqDYPp0
    - focus on L5 "Gradient Descent"
    - focus on L6 "Automatic Differentiation" (should contain a discussion about backprop)
- read up on segmentation and the Unet
- read the iUnet paper