Diana Triantafyllidou & Raphael Vorias

This project is divided into four main sections:

1. A Convolutional Auto-Encoder
2. A Multi-label Classifier
3. A Dual AE/Classifier
4. An Image Segmentator

Each section consists of build files that constuct a model and save it locally. Then, pipeline modules use the built models in order to train. Images and masks are preproccesed and then handled via flow_from_dataframe.

Some sections, such as the AE section, contain visualization files.

While these models are not getting state of the art results, the

Tested models:

Example reconstruction:

Three variations were tested:

1. Baseline Scratch: baseline architecture with reinitialized weights.
2. Baseline Finetune: trained encoder part of the AE to finetune these pre-trained weights.
3. Baseline Frozen: trained encoder part of the AE which are frozen, only the last dense layers are trained.

Next to self-made models, U-Net architectures were used.

This network has both an AutoEncoder and a Classifier and are trained simultaneously.

Weight plots of the first convolutional layer:

Untrained	Trained

TSNE-plot of the final dense layer:

Confusion plot after 250 epochs:

Trained using Dice Loss. Examples: