Domain Adaptation and Transfer Learning in Medical Imaging

The field of medical imaging is witnessing significant advancements in domain adaptation and transfer learning, enabling more accurate and efficient diagnostics. Researchers are focusing on developing innovative methods to address the challenges of domain shift and limited annotated data. One notable direction is the integration of continual learning with multiple instance learning, allowing models to adapt to evolving datasets and improving their performance on large-scale, weakly annotated clinical datasets. Another area of research is the development of geometry-aware manifold alignment methods, which aim to bridge the gap between source and target domains by exploiting geometric information and structured adversarial perturbations. These approaches have shown promising results in improving cross-domain generalization, robustness, and manifold alignment capability. Noteworthy papers in this area include:

  • A paper proposing Attention Knowledge Distillation and Pseudo-Bag Memory Pool to improve continual learning in multiple instance learning models.
  • A paper introducing a discriminator-free approach for unsupervised domain adaptation in multi-label image classification, which achieved state-of-the-art results on several benchmarks.
  • A paper proposing GAMA++, a novel framework for disentangled geometric alignment with adaptive contrastive perturbation, which sets a new standard for semantic geometry alignment in transfer learning.

Sources

Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging

Domain Adaptation for Multi-label Image Classification: a Discriminator-free Approach

Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation

GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation

GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer

Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification

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