Enhancing Anomaly Detection in Histopathological Images Using Convolutional Neural Networks and Variational Autoencoders

Abstract

This paper investigates the enhancement of anomaly detection in histopathological images by integrating Convolutional Neural Networks (CNNs) with Variational Autoencoders (VAEs). Traditional methods for analyzing histopathological images often face challenges in accurately identifying abnormalities due to the complexity and variability inherent in biological tissues. Our approach leverages the hierarchical feature extraction capabilities of CNNs along with the generative prowess of VAEs to improve detection accuracy. The CNN component is optimized to capture multiscale features essential for distinguishing subtle pathological deviations, while the VAE is trained to learn a compact representation of normal tissue structure, thereby facilitating the detection of anomalies through reconstruction errors. We evaluate our method using a curated dataset of histopathological images, demonstrating that the integration of these architectures leads to a significant improvement in the precision and recall of anomaly detection compared to standalone models. The proposed framework not only improves diagnostic accuracy but also reduces false positive rates, thereby offering a robust tool for pathologists. This study underscores the potential of merging deep learning architectures to advance medical image analysis and sets the stage for future exploration into real-time clinical applications.