  We propose a deep clustering architecture alongside image segmentation for
medical image analysis. The main idea is based on unsupervised learning to
cluster images on severity of the disease in the subject's sample, and this
image is then segmented to highlight and outline regions of interest. We start
with training an autoencoder on the images for segmentation. The encoder part
from the autoencoder branches out to a clustering node and segmentation node.
Deep clustering using Kmeans clustering is performed at the clustering branch
and a lightweight model is used for segmentation. Each of the branches use
extracted features from the autoencoder. We demonstrate our results on ISIC
2018 Skin Lesion Analysis Towards Melanoma Detection and Cityscapes datasets
for segmentation and clustering. The proposed architecture beats UNet and
DeepLab results on the two datasets, and has less than half the number of
parameters. We use the deep clustering branch for clustering images into four
clusters. Our approach can be applied to work with high complexity datasets of
medical imaging for analyzing survival prediction for severe diseases or
customizing treatment based on how far the disease has propagated. Clustering
patients can help understand how binning should be done on real valued features
to reduce feature sparsity and improve accuracy on classification tasks. The
proposed architecture can provide an early diagnosis and reduce human
intervention on labeling as it can become quite costly as the datasets grow
larger. The main idea is to propose a one shot approach to segmentation with
deep clustering.
