Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (2203.00585v1)

Abstract: Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology. However, whole-slide imaging (WSI) is a complex computer vision in which: 1) WSIs have enormous image resolutions with precludes large-scale pixel-level efforts in data curation, and 2) diversity of morphological phenotypes results in inter- and intra-observer variability in tissue labeling. To address these limitations, current efforts have proposed using pretrained image encoders (transfer learning from ImageNet, self-supervised pretraining) in extracting morphological features from pathology, but have not been extensively validated. In this work, we conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks. Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images wherein the different attention heads learn distinct morphological phenotypes. We make evaluation code and pretrained weights publicly-available at: https://github.com/Richarizardd/Self-Supervised-ViT-Path.

• The paper introduces a self-supervised ViT framework using DINO to learn interpretable histopathologic features.
• It overcomes whole-slide imaging challenges by incorporating transfer learning and data augmentation to address labeling variability.
• The study shows DINO’s performance advantage over SimCLR, paving the way for improved automated cancer diagnostics.

Self-Supervised Vision Transformers for Histopathology Image Analysis

This paper presents a detailed examination of self-supervised learning methods applied to vision transformers within the context of histopathology images. The work particularly focuses on advancing tissue phenotyping, a critical task in computational pathology crucial for the objective characterization of histopathologic biomarkers related to cancer.

Challenges in Whole-Slide Imaging

Whole-slide imaging (WSI) presents unique challenges due to the enormous image resolutions, making large-scale pixel-level data curation impractical. Furthermore, the diverse morphological phenotypes result in significant inter- and intra-observer variability in tissue labeling, posing additional obstacles for automated approaches. The authors address these constraints by exploring pre-trained image encoders, leveraging transfer learning from ImageNet and employing self-supervised pretraining techniques.

Vision Transformers and Self-Supervised Learning

A notable contribution of the paper is the implementation of Vision Transformers (ViTs) using DINO-based knowledge distillation for learning representations of histopathology images. DINO, a method known for its efficacious self-supervision, allows ViTs to efficiently learn interpretable features that represent distinct morphological phenotypes, each attention head focusing on different aspects of tissue constitution. The empirical findings reveal that ViTs can effectively capture key histopathologic features through their self-attention mechanisms.

Comparative Analysis and Benchmarking

The paper conducts an extensive comparison of different self-supervised learning algorithms, including SimCLR and DINO, across a variety of tasks associated with patch-level and weakly-supervised tissue phenotyping. Results indicate a performance advantage for DINO in most tasks, attributed to its local-global correspondence through data augmentation strategies, which appear particularly beneficial given the inherent hierarchical structure of histopathological imaging data.

Implications and Future Work

The practical implications of this research are significant for computational pathology. By utilizing self-supervised learning, particularly through the DINO framework, ViTs can achieve robust, interpretable, and efficient analysis of tissue phenotypes. This advancement has the potential to inform clinical diagnostics and prognostics by reducing observer variability and enhancing the accuracy of tissue classification tasks.

Theoretically, this work suggests that ViTs are capable of learning complex biological visual concepts without supervision, anticipating a broader application for various domains beyond pathology. Future research should extend these methods to rare and understudied diseases, where limited data naturally poses constraints, to explore the generalizability and scalability of these self-supervised techniques. The development of domain-specific encoders will be crucial to surmount the current limitations and further our capacity to identify novel biomarkers.

Conclusion

This comprehensive evaluation of self-supervised vision transformers reinforces the promising application of DINO and ViTs in computational pathology. The paper's outcomes signify a meaningful step toward automated, interpretable, and scalable analysis in medical image processing, paving the way for more nuanced and reliable histopathological assessments in cancer research.