Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Varun Kumar Dasoju, Qingsu Cheng, Zeyun Yu

TL;DR

To address the bottleneck of annotating breast tissue images under extreme data constraints, the authors achieve 95.5% Dice on 599 training images. They combine a quantum-inspired edge enhancement using a bank of multi-scale Gabor filters with a stabilized, adaptive loss and a complexity-weighted sampling strategy within a pretrained EfficientNet-B7/UNet++ framework, plus a learnable 4-to-3 channel projection. The training pipeline employs OneCycleLR and exponential moving average validation to ensure stability on a small validation set, achieving Dice 95.5% ±0.3% and IoU 91.2% ±0.4%, with boundary accuracy gains of 2.1% and small-lesion gains of 3.8%. The study demonstrates robust performance across diverse cell configurations and discusses clinical implications and avenues for 3D and multi-institutional extensions.

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.

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

TL;DR

To address the bottleneck of annotating breast tissue images under extreme data constraints, the authors achieve 95.5% Dice on 599 training images. They combine a quantum-inspired edge enhancement using a bank of multi-scale Gabor filters with a stabilized, adaptive loss and a complexity-weighted sampling strategy within a pretrained EfficientNet-B7/UNet++ framework, plus a learnable 4-to-3 channel projection. The training pipeline employs OneCycleLR and exponential moving average validation to ensure stability on a small validation set, achieving Dice 95.5% ±0.3% and IoU 91.2% ±0.4%, with boundary accuracy gains of 2.1% and small-lesion gains of 3.8%. The study demonstrates robust performance across diverse cell configurations and discusses clinical implications and avenues for 3D and multi-institutional extensions.

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.

Paper Structure

This paper contains 33 sections, 8 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deep Learning in Medical Image Segmentation
  4. Medical Image Segmentation Architectures
  5. Attention Mechanisms in Segmentation
  6. Loss Functions for Class Imbalance
  7. Training Stability in Deep Learning
  8. Our Approach
  9. Problem Formulation
  10. Dataset Description
  11. Network Architecture
  12. EfficientNet-B7 Encoder
  13. Input Projection Layer
  14. Decoder: U-Net++ with SCSE
  15. Quantum-Inspired Edge Enhancement
  16. ...and 18 more sections

Figures (3)

  • Figure 1: Enhanced architecture combining EfficientNet-B7 encoder, U-Net++ decoder with nested skip connections, and SCSE attention modules.
  • Figure 2: Training progression showing (a) Loss curves, (b) Dice scores with 94% and 95% target lines, (c) IoU curves
  • Figure 3: Qualitative comparison showing improved boundary detection and small lesion segmentation with enhanced linear contrast input images for a better perspective in Top (Best Predictions) and Bottom (Worst Predictions) (a) Distributed Cell Cluster (Dice: 0.9189), (b) Dense Cell Configuration (Dice: 0.9659), (c) Compact Dense Cluster (Dice:0.9084), (d) Sparse Ring Configuration (Dice:0.9398)