A 3D intensity imaging for stainless histopathology

Histopathological image analysis is a cornerstone of cancer diagnosis, but its effectiveness is often limited by variability in staining protocols and imaging conditions across laboratories. This paper presents a novel 3D intensity-based stainless imaging framework integrates stain normalization and deep learning to standardize tissue visualization and improve diagnostic accuracy. Our method transforms conventional 2D histopathology images into 3D intensity maps, leveraging the Beer-Lambert law for stain normalization to mitigate staining variability while preserving critical tissue architecture. We validate our approach on the 305 randomly selected samples from LC-25000 (benign and malicious colon histopathology images) using Structural Similarity Index (SSIM) to quantify preservation of diagnostically relevant structures. Results demonstrate high SSIM scores for normalized 2D images (0.92 ± 0.03) and 3D reconstructions (0.88 ± 0.05), confirming structural fidelity during dimensionality expansion. The 3D intensity maps serve as input to a 3D convolutional neural network (CNN), enabling robust feature learning and achieving superior accuracy compared to traditional 2D methods.