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I'm planning on training a CNN on CT scans for classification. The problem is CT scans are taken slice by slice, and in a typical scan, there could be more than 200 slices. The number of slices in a scan isn't uniform and depend on the scanning machine and age of the person(for whom the scan is taken).

1)How should I make the number of slices uniform for feeding to a deep learning network?

This sorta problem is handled in NLP by padding a chosen vector( or something similar) to sentences which have lengths less than the predefined length and truncating sentences which have lengths greater than the preset length.

2)Can a similar approach be used to make slices(timesteps) uniform or is there a better way?

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I would use an LSTM-RNN to encode sequences of arbitrary length (length meaning the number of slices) into fixed-size output. It should be very easy to implement it in Keras :)

Then feed the sequences in your CNN.

Check sequence-to-sequence encoder-decoder scheme, like this one.

The same concept is applied with state-of-the-art algorithms that require fixed size inputs, like this one.

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