U-Net: Convolutional Networks for Biomedical Image Segmentation The u-net is convolutional network architecture for fast and precise segmentation of images. Up to now it has outperformed the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. It has won the Grand Challenge for Computer-Automated Detection of Caries in Bitewing Radiography at ISBI 2015, and it has won the Cell Tracking Challenge at ISBI 2015 on the two most challenging transmitted light microscopy categories (Phase contrast and DIC microscopy) by a large margin.
https://www.kaggle.com/c/data-science-bowl-2018 Spot Nuclei. Speed Cures. Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The 2018 Data Science Bowl offers our most ambitious mission yet: create an algorithm to automate nucleus detection.
We’ve all seen people suffer from diseases like cancer, heart disease, chronic obstructive pulmonary disease, Alzheimer’s, and diabetes. Many have seen their loved ones pass away. Think how many lives would be transformed if cures came faster.
By automating nucleus detection, you could help unlock cures faster—from rare disorders to the common cold. Want a snapshot about the 2018 Data Science Bowl? View this video.
Alot of the work in this notework was provided by Kjetil Åmdal-SævikKeras who made the "U-Net starter - LB 0.277" the top rated Kernel on Kaggle. https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277
Dataset can be dowloaded here: https://www.kaggle.com/c/data-science-bowl-2018/data