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researchmind.ai / library / transformers-in-medical-imaging
Literature review · draft

Vision Transformers in Medical Imaging: a structured review of 47 studies

47
Papers cited
12
Datasets
3.2k
Source spans

Across the 47 reviewed studies, ViT-based architectures matched or exceeded CNN baselines on classification of retinal pathologies[12][28], while remaining sensitive to pre-training scale.

However, performance gains diminished on smaller annotated corpora — a pattern consistent with findings on cross-domain transfer[34].

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How does masked autoencoding compare to contrastive pretraining for medical images?
ResearchMind · grounded in 6 papers

Masked autoencoding (MAE) tends to outperform contrastive methods on segmentation when labelled data is scarce[3], largely because the reconstruction objective preserves fine-grained spatial detail. Contrastive pretraining[5]remains stronger for classification of well-curated cohorts.

[3]MAE for chest CT pretraining
Liu, Schultz · CVPR 2024
[5]SimCLR on histopathology benchmarks
Vasquez et al. · Nature Methods 2023
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