Hybrid CNN-Transformer Models in Histopathology Image Analysis: A Scoping Review
D L L Oliveira, Tháına A. A. Tosta, Leandro Alves Neves, Marcelo Zanchetta do Nascimento
Abstract
Hybrid convolutional neural networks (CNNs) and Transformer-based architectures have demonstrated strong potential in histopathological image analysis by combining local feature extraction with global context modeling. This dual capability is critical in computational pathology, where accurate interpretation depends on both fine-grained morphological details and broad tissue-level patterns. To systematically assess how these hybrid models have been applied, this scoping review maps the current landscape of CNN-Transformer architectures across classification, segmentation, and detection tasks in histopathological images. Following the PRISMA-ScR guidelines and the Joanna Briggs Institute methodology, a comprehensive literature search was conducted across Web of Science, IEEE Xplore, Scopus, and PubMed. A total of 39 peer-reviewed journal articles published from 2020 onward met the inclusion criteria. To guide the analysis, a task-specific taxonomy was proposed to classify hybrid architectures based on their primary application, offering a structured framework for model comparison and evaluation. Thus, the review synthesizes key aspects of model implementation, including architectural designs, training strategies, dataset characteristics, and evaluation protocols. Findings reveal that hybrid models frequently outperform conventional approaches; however, critical limitations persist, particularly regarding external validation, model interpretability, and reproducibility. Notably, only a small subset of studies employed rigorous validation schemes or integrated explainability into clinical workflows. Moreover, by consolidating evidence and exposing methodological gaps, this review contributes a comprehensive foundation for advancing hybrid deep learning models in digital pathology. It offers actionable insights to inform the development of scalable, transparent, and clinically aligned computational tools for histopathological image analysis.