Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis
Journal:
arXiv
Published Date:
Apr 18, 2025
Abstract
Neuroblastoma, adrenal-derived, is among the most common pediatric solid
malignancies, characterized by significant clinical heterogeneity. Timely and
accurate pathological diagnosis from hematoxylin and eosin-stained whole-slide
images is critical for patient prognosis. However, current diagnostic practices
primarily rely on subjective manual examination by pathologists, leading to
inconsistent accuracy. Existing automated whole-slide image classification
methods encounter challenges such as poor interpretability, limited feature
extraction capabilities, and high computational costs, restricting their
practical clinical deployment. To overcome these limitations, we propose
CMSwinKAN, a contrastive-learning-based multi-scale feature fusion model
tailored for pathological image classification, which enhances the Swin
Transformer architecture by integrating a Kernel Activation Network within its
multilayer perceptron and classification head modules, significantly improving
both interpretability and accuracy. By fusing multi-scale features and
leveraging contrastive learning strategies, CMSwinKAN mimics clinicians'
comprehensive approach, effectively capturing global and local tissue
characteristics. Additionally, we introduce a heuristic soft voting mechanism
guided by clinical insights to bridge patch-level predictions to whole-slide
image-level classifications seamlessly. We verified the CMSwinKAN on the
publicly available BreakHis dataset and the PpNTs dataset, which was
established by our hospital. Results demonstrate that CMSwinKAN performs better
than existing state-of-the-art pathology-specific models pre-trained on large
datasets. Our source code is available at
https://github.com/JSLiam94/CMSwinKAN.
