Branch: Iterative

This branch is created for the iterative approach.

Implementation of deep learning framework -- Unet, using Keras

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation.

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Overview

Data

The original dataset is from isbi challenge, and I've downloaded it and done the pre-processing.

Data augmentation

Model

This deep neural network is implemented with Keras functional API, which makes it extremely easy to experiment with different interesting architectures.

Output from the network is a 512*512 which represents mask that should be learned. Sigmoid activation function makes sure that mask pixels are in [0, 1] range.

Training

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How to use

Dependencies

Run main.py

Or follow notebook trainUnet

Results

About Keras

Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). supports both convolutional networks and recurrent networks, as well as combinations of the two. supports arbitrary connectivity schemes (including multi-input and multi-output training). runs seamlessly on CPU and GPU. Read the documentation Keras.io

Keras is compatible with: Python 2.7-3.5.