Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

Noise in histopathology images from hardware limitations, preparation artifacts, and environmental factors complicates disease analysis and increases risks. With growing workloads and the complexity of histopathology images, developing efficient and precise histopathology image analysis methods is essential. However, many denoising models struggle to extract spatial features and are computationally expensive, primarily due to the limited capacity of convolutions to capture visual patterns across spatial locations, and tend to occupy the largest share of computational costs. In histopathology, many spatial features, such as anomalies or microorganisms, located sparsely across an image, are crucial for the final diagnosis, and many denoising processes often either blur them or introduce artifacts. In this study, we propose a lightweight autoencoder (43.11 kilobytes) for denoising histopathology images by fusing a single involution layer within a small convolution model, resulting in better denoising performance in a hybrid model, which has both channel-specific and location-specific feature extraction capabilities. Building upon the idea of a shallow autoencoder, our model results in much lower memory and compute overhead requirements, while also not avoiding the generation of artifacts. On Malaria Blood Smear and CRC datasets, SSIM Loss and Peak-Signal-to-Noise-Ratio were used for performance evaluation, with lower SSIM Loss (0.058 and 0.34) in denoising images with an added Gaussian noise of 0.3. Our proposed autoencoder, with low weight parameters of 11,037 and 81,630,000 floating point operations (FLOPs), is over 20 times less computationally expensive than Xception, the second-best performing model, establishing ours as the most efficient denoising autoencoder for histopathology images.