Applying CNN for Survival Analysis on DICOM Image Data

Question:

I have a dataset of medical images in DICOM $(.$dcm$)$ format. The data is structured as follows:

Each patient has a unique ID$,$ represented by a folder.

Each folder $($ID$)$ contains between $100$ to $130$ DICOM image files.

There are $60$ patient IDs in total.

For each ID$,$ I have corresponding survival data $($events and times$).$

I want to apply a Convolutional Neural Network $($CNN$)$ model to perform survival analysis on this data. Can you provide a step$-$by$-$step guide with Python code to accomplish this task? Please include the following steps:

Loading and preprocessing the DICOM images

Preparing the survival data

Building and training a CNN model suitable for this task

Integrating the CNN output with survival analysis techniques

Please provide explanations for each step and any necessary code snippets. Also, address any potential challenges or considerations when working with this type of data and analysis.

Note: I$\text{'}$m particularly interested in how to handle the variable number of images per patient and how to effectively use this information in the CNN model for survival analysis.