Deep Learning

Making the Impossible, Possible

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Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial neural networks with representation learning.

MIPAR architectures such as deep neural networks, have been applied to fields including computer vision, bioinformatics, drug design, medical image analysis and material inspection, where they have produced results comparable to and surpassing human expert performance.

What usually took hours by hand can now automatically be analyzed in batches, containing hundreds of images and giving you results in just seconds.

how deep learning works

Trace

Trace features on a few images. Even use tracings you already have.

Train

A deep neural network learns how to recognize what you’ve traced.

Apply

Run your model on a new image to detect complex features.

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Deep Learning in Microstructure Analysis: Real-World Applications

Examples of Deep learning

Detecting grains while ignoring twins
Detecting grains while ignoring twins has challenged the community for decades.

Model trained on 25 images in 40 minutes on a GPU. Applied to new image in 2 seconds.

Conventional methods struggle with low contrast and noise
Conventional methods struggle with low contrast and noise in these complex features.

Model trained on 16 images in 60 minutes on a GPU. Applied to new image in 3 seconds.

Detecting cells
Detecting these cells was impossible for traditional approaches, and even some humans.

Model trained on 600 images in 20 hours on a GPU. Applied to new image in 2 seconds.

Overlapping nanofiber networks have little contrast with the background
Complex, overlapping nanofiber networks have little contrast with the background.

Model trained on 36 images in 40 minutes on a GPU. Applied to new image in 1.5 seconds.

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