Intelligent classification of lung cancer pathology images through comparative morphological feature learning.

Background: The accurate classification of lung cancer pathology images is of paramount importance for both diagnostic and therapeutic purposes. However, the development of robust classification models is often hindered by the intricate cellular morphologies and the scarcity of labeled images, which is a critical bottleneck in the field.

Objectives: The study is designed to incorporate unlabeled data into the training process, thereby enhancing the classification of lung cancer pathology images through the use of comparative learning techniques.

Methods: A methodology is introduced wherein confidently classified unlabeled images are integrated with labeled ones, enriching the training dataset. This approach draws on principles of farthest and nearest neighbor contrastive learning to cultivate a more challenging learning environment and to augment the variability of contrastive samples. To effectively extract key cellular morphological features, an encoder based on the ResNet50 architecture, fortified with deformable and dynamic convolutional techniques, is utilized.

Results: Demonstrated by experimental results, the proposed classification strategy achieves a significant improvement in the accuracy of lung cancer image classification, even under conditions characterized by a limited availability of labeled data, thus underscoring the robustness of the method.

Conclusion: The integration of comparative learning with both labeled and unlabeled images, complemented by the application of advanced convolutional techniques, is shown to be a promising avenue for enhancing the classification of lung cancer pathology images. This research is presented as a practical solution to the urgent need for accurate and efficient diagnostic tools in the field of oncology.