ritika26 / deep_learning_for_biologists_with_keras

tutorials made for biologists to learn deep learning

Deep Learning for Biologists with Keras

By whom?

Yosuke Toda (tyosuke-at-aquaseerser.com)

JST PRESTO researcher at Nagoya Univeristy / Agri-Heir Co., Ltd.

What is it?

• Tutorials that performs deep learning based analysis (mainly) of biological relavent themes. Should give you (biologists) a better implementation of DL much more than general tutorial tasks like MNIST and CIFAR-10. (Although going to prepare basic tutorial section on how to use colab and keras with such in the future)
• Google Colaboratory based notebooks. All you need is internet connection, google chrome browser, and google account. GPU learning environment at a click!
• To open the notebook, click on the button in each section. Logging into Google Account and copying the ipynb to your local google doc folder is preferred.

Note

• Mathmatical calculations and/or theoretical backgrounds will not be thoroughly explained in this tutorial. The object of this notebook is to get a overview of how we can perform DL in the field of biology (especially in plant science and agriculture) for non informatitians.
• Keras with Tensorflow background is the main DL framework used in the notebook. I do not intend to mix different frameworks for clarity in the current situation.
• Feedbacks and requests, complements including typos and misusage of codes in the notebooks are highly welcomed in the issues of github repo.
• A lot of stuff in this notebook is still in alpha ver. (code readability, comments). But to get early feedbacks, opening them cowardly.

To do

• Revise codes and comments for clarity

• Add more examples (described in the "Notebooks to be opened" section)

• Add at a glance slide per each notebook so its object will be clear

Notebooks Open

Rice Seed integrity

An introductory notebook to deep learning based image analysis as well as comparing it with classical machine learning algorithms, furthermore with complete manual image analysis. The object of this notebook is to give the readers an implementation of; What does "Representative Learning" actually mean? What is Feature Selection? Images of rice seeds were provided from Dr. S. Nishiuchi at Nagoya Univ. in 2016 (personal communication).

Refurnish Codes and Comments

17 Flowers dataset

Will build a convolutional neural network (CNN) based classification model using a 17 category flower dataset provided by the team at University of Oxford (http://www.robots.ox.ac.uk/~vgg/data/flowers/17/). The dataset provides of 80 images per category. We will compare the training process starting from scratch (de novo), transfer-learning and fine-tuning which the later two are pretrained with ImageNet Dataset. We will see that upon training with not so much data (for CNN), pretraining has a great effect upon speed and (ocasionally) accuracy of the model.

Refurnish Codes and Comments

Crop/Weed Segmentation

In this notebook, we will perform a segmentation of crop and weed region from images taken by an autonomous field robot, which the datasets are provided from Haug et al., "A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks" (2015). First of all, with conventional approaches, we possibly can isolate the weed and crop resions from the soil area using a color threshold in the green domain. However, how can we further classify the weed (red) from the crop (green) region? Such feature selection is a master of a master craftsmanship. Instead, we will use DL, in specific, semantic segmentation methods to 1) Isolate the grass regions from the soil, 2) Isolating and classifying weeds and crops regions. A neural network architecture named U-Net will be used here.

Need to add commentary throughout the notebook.

Yeast GFP protein localization

Need to add commentary throughout the notebook.

Simulated ChIP-seq motif extraction

Need to add commentary throughout the notebook.

Notebooks To be Opened

• Crop disease diagnosis interpretability (currently under revision in peer reviewed journal)

• Bamboo forest area identification from satelite images

• Arabidopsis Leaf Counting

• Stomatal Aperture Quantification pipeline

• GAN of somekind

• Pix2pix for microscope image alternation