mlsanityData to 14 Oct 2026

Adaptive Domain Generalization for Digital Pathology Images

arXiv 2305.05100 ↗First posted 9 May 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

In AI-based histopathology, domain shifts are common and well-studied. However, this research focuses on stain and scanner variations, which do not show the full picture -- shifts may be combinations of other shifts, or "invisible" shifts that are not obvious but still damage performance of machine learning models. Furthermore, it is important for models to generalize to these shifts without expensive or scarce annotations, especially in the histopathology space and if wanting to deploy models on a larger scale. Thus, there is a need for "reactive" domain generalization techniques: ones that adapt to domain shifts at test-time rather than requiring predictions of or examples of the shifts at training time. We conduct a literature review and introduce techniques that react to domain shifts rather than requiring a prediction of them in advance. We investigate test time training, a technique for domain generalization that adapts model parameters at test-time through optimization of a secondary self-supervised task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 9 May 2023.

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23 of 42 references.

References · 23

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11 more
  1. 2018Do CIFAR-10 Classifiers Generalize to CIFAR-10?cited by 200
  2. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 173
  3. 2017Demystifying Neural Style Transfercited by 152
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  6. 2020Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotationscited by 89
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  8. 2018Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networkscited by 57
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  10. 2021Quantifying the Scanner-Induced Domain Gap in Mitosis Detectioncited by 16
  11. 2021Limitations of Post-Hoc Feature Alignment for Robustnesscited by 14

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