mlsanityData to 7 Oct 2026

Domain Generalization in Computational Pathology: Survey and Guidelines

arXiv 2310.19656 ↗First posted 30 Oct 2023eess.IV cs.CV cs.LGcited by 29

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Abstract (arXiv)

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause domain shift (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative domain generalization (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 113 of its 257 references that are in the map. First posted on arXiv 30 Oct 2023.

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113 of 257 references.

References · 113

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38 more
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Citing papers per year: 2023: 1; 2024: 5; 2025: 12; 2026: 11.

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  1. 2026Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Modelscited by 9
  2. 2024Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shiftscited by 8
  3. 2024StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Imagescited by 6
  4. 2024Benchmarking Domain Generalization Algorithms in Computational Pathologycited by 3
  5. 2025Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillationcited by 2
  6. 2024Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohortcited by 1
  7. 2024Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathologycited by 1
  8. 2025MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classificationcited by 1
  9. 2025Tissue Aware Nuclei Detection and Classification Model for Histopathology Imagescited by 1
  10. 2026Benchmarking Pathology Foundation Models for Breast Cancer Survival Predictioncited by 1
  11. 2023Transformer-based Model for Oral Epithelial Dysplasia Segmentationcited by 0
  12. 2025Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancementcited by 0
17 more
  1. 2025A Novel Approach to Linking Histology Images with DNA Methylationcited by 0
  2. 2025Staining normalization in histopathology: Method benchmarking using multicenter datasetcited by 0
  3. 2025A multi-task neural network for atypical mitosis recognition under domain shiftcited by 0
  4. 2025CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shiftscited by 0
  5. 2025Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignmentcited by 0
  6. 2025CrossMed: A Multimodal Cross-Task Benchmark for Compositional Generalization in Medical Imagingcited by 0
  7. 2025All Centers Are at most a Few Tokens Apart: Knowledge Distillation with Domain Invariant Prompt Tuningcited by 0
  8. 2025Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimizationcited by 0
  9. 2026Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classificationcited by 0
  10. 2026Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentationcited by 0
  11. 2026The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Modelscited by 0
  12. 2026Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Trainingcited by 0
  13. 2026Robustifying pathology foundation models via fine-tuningcited by 0
  14. 2026Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancercited by 0
  15. 2026Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendationscited by 0
  16. 2026TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation Modelscited by 0
  17. 2026Domain Generalization for Smartphone-Based Human Activity Recognition: A Systematic Analysis of Components and Interactionscited by 0