Is there a way to visualise what/how my image classifier has identified an image?

Question

Using tensorflow. If I have trained my image classifier many images of a close up of melanomas and other skin cancers.

Then ask it to classify using the trained model a photo that has another element for example melanoma on a hand (with other elements that look like melanoma in the background example a hypothetical tablecloth that has melanoma patterns).

Is there a way to visualize what the image classifier has identified as melanoma ? Outputting a circled/highlighted the found melanomas in the photo?

Answer 1

What you are looking for is a model with an "Attention mechanism". Unfortunately, this means that you have to change your model and to retrain it. It is more complicated that a simple image classifier but I can assure you that it is worth it to learn how it works. Or you can simply find an existing repository that implement it.

Alternatively, you can train your model on smaller images that contain exactly the melanoma. The preprocessing will be much heavier. Then you will scan your bigger images with a bounding box. You will have probabilities for each small images.

Answer 2

Actually you don't need to retrain the model, you could use deconvolution networks (see this video of Matthew Zeiler explaining this paper: Visualizing and Understanding Deep Neural Networks) or get inspiration with this paper: Methods for Interpreting and Understanding Deep Neural Networks.