Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Deep learning is increasingly used for epithelial-stroma (ES) classification in medical imaging.
• Traditional methods fail when training and test data distributions differ, common in complex imaging.
• Unsupervised domain adaptation (UDA) offers a solution by transferring knowledge to unlabeled data, avoiding extensive manual annotation.

Purpose of the Study:

• To address the limitation of existing UDA methods that ignore semantic alignment in ES classification.
• To propose a novel deep learning framework, the Curriculum Feature Alignment Network (CFAN), for improved ES classification.
• To enhance the transferability of deep learning models across different medical imaging datasets.

Main Methods:

• Developed a Curriculum Feature Alignment Network (CFAN) to gradually align discriminative features across domains.
• Implemented a Curriculum Transfer Strategy (CTS) for effective sample selection from the target domain.
• Utilized Adaptive Centroid Alignment (ACA) to minimize intra-class differences during iterative training.

Main Results:

• CFAN demonstrated superior performance in epithelial-stroma (ES) classification compared to standard deep learning and existing UDA methods.
• Experiments were validated on three independent public ES datasets, confirming the method's robustness.
• The proposed approach effectively aligns features and reduces domain discrepancies in medical image classification.

Conclusions:

• The Curriculum Feature Alignment Network (CFAN) significantly advances unsupervised domain adaptation for ES classification tasks.
• CFAN's iterative approach with CTS and ACA provides a robust solution for handling domain shifts in medical imaging.
• This work offers a promising direction for developing more accurate and adaptable deep learning models in clinical applications.