Question $1(15$ marks$)$

In the healing of wounds there are four named stages, $($i$)$ Hemostatis, $($ii$)$ inflammation, $($iii$)$ proliferation, and

$($iv$)$ remodelling. Each of these stages is characterised by some identifiable texture that a clinician can use

when reviewing the wound of a patient. A hospital would like to use artificial intelligence as an aid for their

clinicians. You have been approached as a data scientist to design a classifier that can take a wound image

and produce an indication of the stage of healing. Unfortunately, you do not have sufficient example data of

each wound stage to train your classifier model.

$($a$)[5$ marks $]$ Briefly describe a deep neural network $($including layers and their type, loss function, optimi$-$

sation, etc.$)$ that may be suitable to model your classifier. Explain the rationale for your design choices.

You must consider the data structure and other necessary data organisation. Your response should not

be more than one page and limited to topics covered in the CSCl$933$ lectures. Using dot points attract

poor marks.

$($b$)[10$ marks $]$ Because of the lack of sufficient data you may not be able to successfully train a good model.

As a data$-$based regularisation strategy you decided to generate more data. Describe the design of

a variational autoencoder that will be suitable to generate more wound image data. Your design must

include the key subsystems, their specifications and how they work. Your description should include any

constrain $($loss function$)$ required to make your autoencoder work reasonably. Your response MUST NOT

be more than a page and half. You MAY NOT use any concepts not discussed in the CSC$1933$ lectures.

Using dot points attract poor marks.