A Crux on Deep Clustering Neural Networks for Medical Image Classification

In the domain of image classification, researchers have conducted extensive studies on image classification using deep clustering methods based on public large-scale image datasets. Deep clustering research has garnered increasing attention due to its adaptability across a wide range of application domains and scientific fields. However, its application in medical imaging remains relatively underexplored, largely due to the limited availability of high-quality datasets, inherent data irregularities, and significant inter-patient variability, which complicates model generalization. Despite these challenges, deep clustering exhibits strong potential in the medical domain, particularly for patient stratification and the discovery of both high-level and subtle patterns within complex medical datasets. The experimental comparison results can discover clustering neural networks suitable for medical images and verify their potential in the medical domain.