Evaluating Spiking and Non-Spiking Neural Networks for Colorectal Serrated Polyp Subtype Classification

Image classification on digital pathology images relies heavily on convolutional neural networks (CNNs), yet the behavior of alternative neural computing paragigms in this domain remains insufficiently characterized. Spiking neural networks (SNNs), which process information through event-driven spike-based dynamics, have recently become trainable at scale but have not been evaluated under standardized colorectal pathology benchmarks. This study presents the first controlled comparison of SNNs and CNNs on the Minimalist Histopathology Image Analysis (MHIST) Dataset, a widely used publicly available benchmark designed for reproducible evaluation of histopathology classification models released by Dartmouth-Hitchcock Medical Center. The classification task focuses on the clinically important binary distinction between hyperplastic polyps (HPs) and sessile serrated adenomas (SSAs), a challenging problem characterized by substantial inter-pathologist variability, where HPs are typically benign and SSAs represent precancerous lesions requiring closer clinical follow-up. Histologically, HPs exhibit superficial serrated architecture and elongated crypts, whereas SSAs are characterized by broad-based, often complex crypt structures with pronounced serration. A conventional ResNet-18 architecture and its spiking counterpart are evaluated under matched training and inference to isolate the effect of spiking computation. Models performance is quantified using the area under the receiver operating characteristic curve (ROC-AUC), yielding 0.817 for the conventional CNN and 0.812 for the SNN. This comparison enables a direct assessment of how spiking computation influences discriminative performance in HPs versus SSAs binary classification and provides a benchmark reference for SNNs on the MHIST dataset. The code is publicly available at https://github.com/qug125/snn-crcp.