Computational pathology with dynamic convolutional and adaptive kernels

Area of Science:

• Computational pathology
• Medical informatics
• Deep learning in medicine

Background:

• Pathology and lab medicine increasingly rely on data processing and learning.
• Integrating clinical informatics with scientific research enhances patient care.
• Computational pathology merges histopathological images with clinical informatics for advanced analysis.

Purpose of the Study:

• To address limitations of conventional Convolutional Neural Networks (CNNs) in handling morphological heterogeneity in disease tissues.
• To present an optimized Omni-Dimensional Dynamic Convolution (ODConv) network for improved feature extraction from histopathological images.
• To enhance the automated diagnosis of skeletal muscle disorders using advanced deep learning.

Main Methods:

• Developed an optimized variant of Omni-Dimensional Dynamic Convolution (ODConv) networks.
• Implemented multi-dimensional attention across spatial positions, input channels, output channels, and kernel candidates for adaptive feature extraction.
• Evaluated ODConv on skeletal muscle images from amyotrophic lateral sclerosis and Type I diabetes mouse models, using wheat-germ agglutinin and hematoxylin and eosin staining.

Main Results:

• ODConv achieved competitive classification performance without ImageNet pretraining, outperforming seven fine-tuned pretrained architectures.
• Demonstrated the effectiveness of omni-dimensional dynamic kernels in learning discriminative morphological representations directly from domain data.
• Reported strong statistical agreement metrics, effective class balance handling, and stable decision boundaries.

Conclusions:

• ODConv serves as a robust computational pathology framework for automated diagnosis.
• The study validates the capability of ODConv in analyzing diverse histopathological features for disease identification.
• ODConv advances the automated diagnosis of neurodegenerative and metabolic skeletal muscle disorders.