TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology
This work addresses trust and efficiency issues for pathologists in digital pathology, though it appears incremental as it builds on unsupervised methods with uncertainty integration.
The paper tackled the problem of labor-intensive annotation and lack of uncertainty estimation in AI for histopathology by developing TUMLS, a trustful fully unsupervised multi-level segmentation method, which achieved an F1 score of 77.46% and Jaccard score of 63.35% on nucleus segmentation, outperforming other unsupervised approaches.
Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to identify the different tissue types within low-resolution training data. It selects representative patches from each identified group based on an uncertainty measure and then does unsupervised nuclei segmentation in their respective higher-resolution space without using any ML algorithms. Crucially, this solution integrates seamlessly into clinicians workflows, transforming the examination of a whole WSI into a review of concise, interpretable cross-level insights. This integration significantly enhances and accelerates the workflow while ensuring transparency. We evaluated our approach using the UPENN-GBM dataset, where the AE achieved a mean squared error (MSE) of 0.0016. Additionally, nucleus segmentation is assessed on the MoNuSeg dataset, outperforming all unsupervised approaches with an F1 score of 77.46% and a Jaccard score of 63.35%. These results demonstrate the efficacy of TUMLS in advancing the field of digital pathology.