Practical part for AMLD 2021 Workshop based on the original work of Pawel Rosikiewicz available at Github: https://github.com/PawelRosikiewicz/SkinDiagnosticAI
## Introduction
This is a Renku project - basically a git repository with some
bells and whistles. You'll find we have already created some
useful things like `data` and `notebooks` directories and
a `Dockerfile`.
## Working with the project
The simplest way to start your project is right from the Renku
platform - just click on the `Environments` tab and start a new session.
This will start an interactive environment right in your browser.
To work with the project anywhere outside the Renku platform,
click the `Settings` tab where you will find the
git repo URLs - use `git` to clone the project on whichever machine you want.
### Changing interactive environment dependencies
Initially we install a very minimal set of packages to keep the images small.
However, you can add python and conda packages in `requirements.txt` and
`environment.yml` to your heart's content. If you need more fine-grained
control over your environment, please see [the documentation](https://renku.readthedocs.io/en/latest/user/advanced_interfaces.html#dockerfile-modifications).
## Project configuration
Project options can be found in `.renku/renku.ini`. In this
project there is currently only one option, which specifies
the default type of environment to open, in this case `/lab` for
JupyterLab. You may also choose `/tree` to get to the "classic" Jupyter
interface.
## Moving forward
Once you feel at home with your project, we recommend that you replace
this README file with your own project documentation! Happy data wrangling!
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## SkinDiagnosticAI
### Comparison of 5000 AI methods for cancer detection and classyfication on dermatoscopic images from Harvard HAM10000 dataset using FastClassAI workbench
Author: Pawel Rosikiewicz, Founder, and Team Leader at SwissAI
License: MIT
The goal is to quickly test and optimize large number of ml and deep learning models and dataset preprocessing procedures, that are integrated in one python enviroment with FastClassAI workebench.
Main Goals are:
- to identify main challenges with the dataset used for model training,
- to explore different strategies for data preparation, treatment and feature extraction,
- to test, of the shelf AI solutions, with extensive grid search,
- to develope baseline for further analyses,
- to evaluate what statistics and error fucntions shodul be used for developing final and ensemble models,
### Presentation on SkinDiagnosticAI Project
* all images were created wiht FastClassAI pipeline,
* the slides shows full analyis done on over 5000 compared models and data treatment procedures,
* Jupyter notebooks in notebook/ folder shows light vervion of that analyis that can be reapeated by the user and build up to any number of compared models,
