PurposeUncertainty estimation has gained significant attention in recent years for its potential to enhance the performance of deep learning (DL) algorithms in medical applications and even potentially address domain shift challenges. However, it is not straightforward to incorporate uncertainty estimation with a DL system to achieve a tangible positive effect. The objective of our work is to evaluate if the proposed spatial uncertainty aggregation (SUA) framework may improve the effectiveness of uncertainty estimation in segmentation tasks. We evaluate if SUA boosts the observed correlation between the uncertainty estimates and false negative (FN) predictions. We also investigate if the observed benefits can translate to tangible improvements in segmentation performance.ApproachOur SUA framework processes negative prediction regions from a segmentation algorithm and detects FNs based on an aggregated uncertainty score. It can be utilized with many existing uncertainty estimation methods to boost their performance. We compare the SUA framework with a baseline of processing individual pixel’s uncertainty independently.ResultsThe results demonstrate that SUA is able to detect FN regions. It achieved Fβ=0.5 of 0.92 on the in-domain and 0.85 on the domain-shift test data compared with 0.81 and 0.48 achieved by the baseline uncertainty, respectively. We also demonstrate that SUA yields improved general segmentation performance compared with utilizing the baseline uncertainty.ConclusionsWe propose the SUA framework for incorporating and utilizing uncertainty estimates for FN detection in DL segmentation algorithms for histopathology. The evaluation confirms the benefits of our approach compared with assessing pixel uncertainty independently.
KEYWORDS: Cancer detection, Image segmentation, Education and training, Breast cancer, Pathology, Lymph nodes, Histograms, Deep learning, Data modeling, Visualization
Computational pathology, a developing area of primarily deep learning (DL) solutions aiming to aid pathologists at their daily tasks, has shown promising results in research settings. In recent years, uncertainty estimation has gained substantial recognition as having high potential to bring value to DL algorithms for medical applications. But it is not trivial how to incorporate it with a DL system to obtain a real positive impact. In this work we propose a framework to spatially aggregated epistemic uncertainty in order to detect false negatives produced by a segmentation algorithm of breast cancer metastases. We show a strong correlation between the false negative segmentation areas and the aggregated uncertainty values. Furthermore, the results include examples of reducing false negatives, where the uncertainty approach led to detection of some tumour metastases that had been missed.
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