mlsanityData to 14 Oct 2026

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology

arXiv 2506.11439 ↗First posted 13 Jun 2025cs.CVcited by 0

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Abstract (arXiv)

Machine-learning-assisted cancer subtyping is a promising avenue in digital pathology. Cancer subtyping models, however, require careful training using expert annotations so that they can be inferred with a degree of known certainty (or uncertainty). To this end, we introduce the concept of uncertainty awareness into a self-supervised contrastive learning model. This is achieved by computing an evidence vector at every epoch, which assesses the model's confidence in its predictions. The derived uncertainty score is then utilized as a metric to selectively label the most crucial images that require further annotation, thus iteratively refining the training process. With just 1-10% of strategically selected annotations, we attain state-of-the-art performance in cancer subtyping on benchmark datasets. Our method not only strategically guides the annotation process to minimize the need for extensive labeled datasets, but also improves the precision and efficiency of classifications. This development is particularly beneficial in settings where the availability of labeled data is limited, offering a promising direction for future research and application in digital pathology.

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References · 20

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8 more
  1. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 808
  2. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
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  7. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  8. 2023FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learningcited by 1

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