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e572ed01 · e572ed01 · e572ed01 · e572ed01 · e572ed01 · e572ed01
--- a/Figures_for_Jupyter_notebooks/Slide13.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide13.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide14.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide14.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide15.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide15.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide16.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide16.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide17.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide17.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide2.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide2.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide3.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide3.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide4.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide4.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide5.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide5.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide6.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide6.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide7.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide7.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide8.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide8.jpeg
--- a/Figures_for_Jupyter_notebooks/Slide9.jpeg
+++ b/Figures_for_Jupyter_notebooks/Slide9.jpeg
--- a/LICENSE
+++ b/LICENSE
+GPL License
+
+Copyright (c) 2021 Pawel Rosikiewicz
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
\ No newline at end of file
--- a/README.md
+++ b/README.md
 # AMLD 2021 Workshop

-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!
\ No newline at end of file
+Practical part for AMLD 2021 Workshop based on the original work of Pawel Rosikiewicz available at [Github](https://github.com/PawelRosikiewicz/SkinDiagnosticAI).
+  
+![skindiagnosticai title image](images/skindiagnosticai_title_image.png)
+
+## 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  
+
+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,
+
+![skindiagnosticai presentation slide](images/Slide1.png)
+![skindiagnosticai presentation slide](images/Slide2.png)
+![skindiagnosticai presentation slide](images/Slide3.png)
+![skindiagnosticai presentation slide](images/Slide4.png)
+![skindiagnosticai presentation slide](images/Slide5.png)
+![skindiagnosticai presentation slide](images/Slide6.png)
+![skindiagnosticai presentation slide](images/Slide7.png)
+![skindiagnosticai presentation slide](images/Slide8.png)
+![skindiagnosticai presentation slide](images/Slide9.png)
+![skindiagnosticai presentation slide](images/Slide10.png)
+![skindiagnosticai presentation slide](images/Slide11.png)
+![skindiagnosticai presentation slide](images/Slide12.png)
+![skindiagnosticai presentation slide](images/Slide13.png)
+![skindiagnosticai presentation slide](images/Slide14.png)
+![skindiagnosticai presentation slide](images/Slide15.png)
+![skindiagnosticai presentation slide](images/Slide16.png)
+![skindiagnosticai presentation slide](images/Slide17.png)
+![skindiagnosticai presentation slide](images/Slide18.png)
+![skindiagnosticai presentation slide](images/Slide19.png
--- a/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_logfile.csv
+++ b/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_logfile.csv
+batch_size,created_dt,datasetname,img_batch_features_filename,img_batch_info_filename,module_name,module_source
+744,2021-02-11 10:21:10.982713,train_05,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_05_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_05_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+742,2021-02-11 10:23:40.609939,train_02,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_02_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_02_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+740,2021-02-11 10:25:58.727174,valid_01,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_valid_01_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_valid_01_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+742,2021-02-11 10:28:20.782967,train_03,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_03_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_03_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+744,2021-02-11 10:30:45.976417,train_04,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_04_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_04_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+367,2021-02-11 10:31:54.910890,test_01,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+742,2021-02-11 10:34:18.699225,train_01,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_01_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_01_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+738,2021-02-11 10:36:37.716441,train_06,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_06_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_06_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+741,2021-02-11 10:38:58.823356,valid_02,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_valid_02_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_valid_02_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+751,2021-02-11 10:41:15.462112,train_07,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_07_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_train_07_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
+367,2021-02-11 10:42:24.065582,test_02,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_encoded.npy,BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_labels.csv,BiT_M_Resnet101,/Users/pawel/Desktop/Activities/100__Projects/SkinAnaliticAI/models/bit_m-r101x1_1
--- a/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_encoded.npy
+++ b/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_encoded.npy
--- a/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_labels.csv
+++ b/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_01_labels.csv
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--- a/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_encoded.npy
+++ b/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_encoded.npy
--- a/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_labels.csv
+++ b/data/interim/ham10000__cancer_detection_and_classification__extracted_features/BiT_M_Resnet101_HAM10000__Cancer_Detection_And_Classification_test_02_labels.csv
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