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Spot-Nuclei-Speed-Cruice

Detecting Nuclei in Diverse Images Using UNet

Imagine accelerating the diagnosis of nearly every disease, from lung cancer and heart conditions to rare genetic disorders. This project focuses on automating nuclei detection in microscopic images, a critical step in many medical diagnoses.

To tackle this problem, I used the UNet architecture, a powerful deep learning model specifically designed for biomedical image segmentation.

Below is an illustration of the UNet model that I implemented in this project. You can find the source code for the network in the files above. UNet

Dataset

https://drive.google.com/file/d/18ldQQnPeNre23hJSgLZvHAzSlV0i3v-q/view?usp=sharing

Resources

https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
https://www.kaggle.com/c/data-science-bowl-2018/overview

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Find the nuclei in divergent images to using UNet

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