Breast Cell Segmentation Under Extreme Data Constraints: Quantum Enhancement Meets Adaptive Loss Stabilization

2025-12-02

AI Summary

Breast tissue segmentation under extreme data scarcity (599 training images) and severe class imbalance (only 4% foreground pixels; 60% of images contain no breast tissue). Method: We propose a quantum-inspired multi-scale Gabor boundary enhancement module and an adaptive Dice loss, integrated into an EfficientNet-B7/UNet++ framework. Our approach incorporates channel projection, exponential moving average, and statistical outlier detection for robustness, alongside complexity-weighted sampling and automated positive-sample weighting. Contributions/Results: On 599 training and 129 validation images, the model achieves 95.5% ± 0.3% Dice score and 91.2% ± 0.4% IoU—improving boundary accuracy by 2.1% and small-lesion detection rate by 3.8%. The method significantly reduces reliance on expert annotations and advances the practical deployment of medical image segmentation in low-resource settings.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Annotating medical images demands significant time and expertise, often requiring pathologists to invest hundreds of hours in labeling mammary epithelial nuclei datasets. We address this critical challenge by achieving 95.5% Dice score using just 599 training images for breast cell segmentation, where just 4% of pixels represent breast tissue and 60% of images contain no breast regions. Our framework uses quantum-inspired edge enhancement via multi-scale Gabor filters creating a fourth input channel, enhancing boundary detection where inter-annotator variations reach +/- 3 pixels. We present a stabilized multi-component loss function that integrates adaptive Dice loss with boundary-aware terms and automatic positive weighting to effectively address severe class imbalance, where mammary epithelial cell regions comprise only 0.1%-20% of the total image area. Additionally, a complexity-based weighted sampling strategy is introduced to prioritize the challenging mammary epithelial cell regions. The model employs an EfficientNet-B7/UNet++ architecture with a 4-to-3 channel projection, enabling the use of pretrained weights despite limited medical imaging data. Finally, robust validation is achieved through exponential moving averaging and statistical outlier detection, ensuring reliable performance estimates on a small validation set (129 images). Our framework achieves a Dice score of 95.5% +/- 0.3% and an IoU of 91.2% +/- 0.4%. Notably, quantum-based enhancement contributes to a 2.1% improvement in boundary accuracy, while weighted sampling increases small lesion detection by 3.8%. By achieving groundbreaking performance with limited annotations, our approach significantly reduces the medical expert time required for dataset creation, addressing a fundamental bottleneck in clinical perception AI development.

Problem

• Achieving high-accuracy breast cell segmentation with extremely limited training data.
• Addressing severe class imbalance and boundary detection challenges in medical images.
• Reducing annotation time and expertise needed for clinical AI development.

Innovation

• Quantum-inspired edge enhancement via multi-scale Gabor filters
• Stabilized multi-component loss with adaptive Dice and boundary terms
• Complexity-based weighted sampling to prioritize challenging cell regions