Histopathological image classification with unsupervised approaches using deep convolutional autoencoder and k-nearest neighbors
Shamima Nasrin, Md Zahangir Alom, Tarek M. Taha · 2023
Artificial intelligence (AI) based analysis accelerates clinical diagnosis from pathological images efficiently and accurately. Due to the high dimensionality of pathological images, extracting meaningful feature representations of the pixels from high-dimensional images is essential. This can be used for further analysis to obtain better insights. This study used Deep Convolutional Neural Networks (DCNN) and end-to-end Deep Convolutional auto-encoder outcomes (DCAE) models for feature extraction. K-means and K-Nearest Neighbors (KNN) methods were then used for clustering and classification and achieved 95% testing accuracy with these unsupervised classification methods. In addition, t-distributed Stochastic Non-linear Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied for clustering and visualization of different tissue types and demonstrated promising representation for histopathological image clustering of 20 different tissue types. Within-Cluster and Square (WSS) errors were used to determine the optimal number of classes in cluster space with t-SNE and UMAP methods. Most importantly, the proposed system relies on class probability and the visual interpretation that provides the relationship among the 20 different pathological tissue types. The proposed pipeline is potentially applicable for understanding pathological image classification and clustering tasks to obtain better insight into digital pathology applications.