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Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment

Lei Li, Xinglin Zhang, Jun Liang, Tao Chen

TL;DR

This work addresses domain shift and class imbalance in medical imaging, focusing on embryo development assessment across multiple imaging modalities. It proposes Imbalance-Aware Domain Adaptation (IADA), integrating three components: imbalance-aware feature learning, balanced domain alignment via an adversarial discriminator with class-aware weighting, and adaptive thresholding for minority classes. A theoretical analysis derives a generalization bound under imbalance and a convergence rate, and experiments show substantial gains, up to $25.19\%$ in accuracy and up to $12.56\%$ in AUC, across challenging domain shifts. The method achieves robust, equitable performance across domains, and the authors release public code for reproducibility.

Abstract

Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and class imbalance, where certain disease conditions are naturally underrepresented. We present Imbalance-Aware Domain Adaptation (IADA), a novel framework that simultaneously tackles both challenges through three key components: (1) adaptive feature learning with class-specific attention mechanisms, (2) balanced domain alignment with dynamic weighting, and (3) adaptive threshold optimization. Our theoretical analysis establishes convergence guarantees and complexity bounds. Through extensive experiments on embryo development assessment across four imaging modalities, IADA demonstrates significant improvements over existing methods, achieving up to 25.19\% higher accuracy while maintaining balanced performance across classes. In challenging scenarios with low-quality imaging systems, IADA shows robust generalization with AUC improvements of up to 12.56\%. These results demonstrate IADA's potential for developing reliable and equitable medical imaging systems for diverse clinical settings. The code is made public available at \url{https://github.com/yinghemedical/imbalance-aware_domain_adaptation}

Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment

TL;DR

This work addresses domain shift and class imbalance in medical imaging, focusing on embryo development assessment across multiple imaging modalities. It proposes Imbalance-Aware Domain Adaptation (IADA), integrating three components: imbalance-aware feature learning, balanced domain alignment via an adversarial discriminator with class-aware weighting, and adaptive thresholding for minority classes. A theoretical analysis derives a generalization bound under imbalance and a convergence rate, and experiments show substantial gains, up to in accuracy and up to in AUC, across challenging domain shifts. The method achieves robust, equitable performance across domains, and the authors release public code for reproducibility.

Abstract

Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and class imbalance, where certain disease conditions are naturally underrepresented. We present Imbalance-Aware Domain Adaptation (IADA), a novel framework that simultaneously tackles both challenges through three key components: (1) adaptive feature learning with class-specific attention mechanisms, (2) balanced domain alignment with dynamic weighting, and (3) adaptive threshold optimization. Our theoretical analysis establishes convergence guarantees and complexity bounds. Through extensive experiments on embryo development assessment across four imaging modalities, IADA demonstrates significant improvements over existing methods, achieving up to 25.19\% higher accuracy while maintaining balanced performance across classes. In challenging scenarios with low-quality imaging systems, IADA shows robust generalization with AUC improvements of up to 12.56\%. These results demonstrate IADA's potential for developing reliable and equitable medical imaging systems for diverse clinical settings. The code is made public available at \url{https://github.com/yinghemedical/imbalance-aware_domain_adaptation}
Paper Structure (16 sections, 9 theorems, 19 equations, 2 figures, 1 table)

This paper contains 16 sections, 9 theorems, 19 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Domain Shift
  4. Learning with Imbalanced Data
  5. Problem Formulation
  6. Imbalance-Aware Domain Adaptation
  7. Imbalance-Aware Feature
  8. Adversarial Domain Alignment
  9. Imbalance-Aware Classification
  10. Training Objective
  11. Theoretical Analysis
  12. Experiments & Analysis
  13. Experimental Set-Up
  14. Performance
  15. Ablation Study
  16. ...and 1 more sections

Key Result

Theorem 5.1

Let $h \in \mathcal{H}$ be a hypothesis with expected errors $\epsilon_s(h)$ and $\epsilon_t(h)$ on the source and target domains respectively. For any $\delta > 0$, with probability at least $1-\delta$, the following bound holds: where $\lambda$ represents the combined error of the ideal joint hypothesis.

Figures (2)

  • Figure 1: Illustration of the impact of class imbalance on domain shift in embryo classification. The figure shows the transition from high-end to low-end imaging systems and their varying class distributions. Blastocyst refers to a critical stage in early embryo development (typically 5-6 days after fertilization) characterized by the formation of a fluid-filled cavity, while non-blastocyst encompasses earlier developmental stages. The decreasing opacity from left to right represents the degradation in image quality across domains.
  • Figure 2: Ablation study of the effects of regularization coefficient $\lambda_{reg}$ (a) and adversarial coefficient $\lambda_{adv}$ (a) in the objective (\ref{['eqn:objective']}) on the learning process.

Theorems & Definitions (23)

  • Theorem 5.1: Generalization Bound with Class Imbalance
  • proof
  • Remark 5.2
  • Corollary 5.3: Balanced Domain Case
  • Remark 5.4
  • Lemma 5.6: Class-weighted Gradient Bound
  • proof
  • Theorem 5.7: Convergence Rate with Class Imbalance
  • proof
  • Remark 5.8
  • ...and 13 more