Tensorflow - classify based on multiple image as input, not signle one

Question

I'm building CNN that will tell me if a person has brain damage. I'm planning to use tf inception v3 model, and build_image_data.py script to build TFRecord.

Dataset is composed of brain scans. Every scan has about 100 images(different head poses, angles). On some images, damage is visible, but on some is not. I can't label all images from the scan as a damage positive(or negative), because some of them would be labeled wrong(if scan is positive on damage, but that is not visible on specific image).

Is there a way to label the whole scan as positive/negative and in that way train the network? And after training is done, pass scan as input to network(not single image) and classify it.

leonard · Accepted Answer · 2018-02-18 01:46:04Z

2

It looks like multiple instance learning might be your approach. Check out these two papers:

Multiple Instance Learning Convolutional Neural Networks for Object Recognition

Classifying and segmenting microscopy images with deep multiple instance learning

The last one is implemented by @dancsalo (not sure if he has a stack overflow account) here.

I looks like the second paper deals with very large images and breaks them into sub images, but labels the entire image. So, it is like labeling a bag of images with a label instead of having to make a label for each sub-image. In your case, you might be able to construct a matrix of images, i.e. a 10 image x 10 image master image for each of the scans...

Let us know if you do this and if it works well on your data set!

answered Feb 18, 2018 at 1:46

leonard

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