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The goal of this project is to classify the blood clot origins in ischemic stroke. Using whole slide digital pathology images, you'll build a model that differentiates between the two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis.
The model is to be trained on Azure using Data from the repo using streaming client
This repo contains the data and code files for the kaggle competition https://www.kaggle.com/competitions/mayo-clinic-strip-ai.
The data originally present for the challenge is in tiff format and is 356GB in size. For the purpose of this analysis, we downscale the images and store them in png format. The code for this downscaling of data can be found in this repo in data/raw/train/ folder. The downscaled images can be found here. To map the images to the classes they belong to, the competition data provides us with a CSV file that contains information about the data which can be found in data/raw/train.csv
In addition this repo, also contains the Pretrained EfficientNet Models which can be found in data/raw/Pretrained_Efficient_Models/ folder
conda create -n azure_ml python=3.9.13
conda activate azure_ml [Goes into conda Environment ]
pip install azureml-sdk[notebooks,tensorboard,interpret]
Clone the Repo
The code can be found in notebooks/01-aiswarya-ramachandran-connecting-to-azure-ml-sdk.ipynb . This notebooks contains the complete code and internally creates the scripts needed to train the model on Azure.
Article describing this entire process can be found at https://hackernoon.com/image-classification-on-azure-with-dagshub-direct-data-access
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This repo contains the data and code files for the kaggle competition https://www.kaggle.com/competitions/mayo-clinic-strip-ai.
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?