Context-Aware Models and Invariant Representations in Computational Pathology

Advances in Computational Pathology and Whole Slide Image Analysis

Recent developments in computational pathology and whole slide image (WSI) analysis have significantly advanced the field, particularly in the areas of treatment response prediction, foundation model adaptation, hierarchical classification, survival prediction, and invariant shape representation learning. These advancements are characterized by a shift towards more context-aware and task-specific models, which leverage spatial and temporal data to improve diagnostic accuracy and patient outcomes.

One of the key trends is the integration of transformer and graph convolution networks to capture complex histology interactions within tumor microenvironments, enabling more accurate predictions of treatment responses. Additionally, the adaptation of foundation models for specific tasks through concept-guided feature enhancement has shown promise in boosting the performance of pathology models.

Hierarchical classification approaches, which consider the multi-classification of diseases as a binary tree structure, are also gaining traction. These methods use text descriptions to guide the aggregation of hierarchical representations, offering a new perspective on deep learning-assisted solutions for complex WSI classification.

Survival prediction models are evolving to incorporate multi-slide analysis, mimicking clinical practices where pathologists examine multiple cases to enhance assessment. This approach allows for the capture of cross-slide prognostic features, leading to more comprehensive survival risk assessments.

Another notable development is the introduction of invariant shape representation learning, which aims to strengthen the robustness of image classifiers by capturing invariant features in latent shape spaces. This approach addresses the instability of statistical correlations across different environments, leading to more accurate predictions.

Noteworthy Papers:

  • A histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in triple negative breast cancer shows significant potential for personalized treatment strategies.
  • Concept Anchor-guided Task-specific Feature Enhancement significantly boosts the performance of pathology foundation models for specific tasks, demonstrating enhanced expressivity and discriminativeness.
  • Hierarchical classification with PathTree provides a new perspective on deep learning-assisted solutions for complex WSI classification, outperforming state-of-the-art methods.
  • GroupMIL for survival prediction models multiple slides as a single sample, capturing cross-slide prognostic features and outperforming state-of-the-art approaches.
  • Invariant Shape Representation Learning offers a novel framework to enhance the robustness of image classifiers by capturing invariant features in latent shape spaces.

Sources

NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement

Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image

Look a Group at Once: Multi-Slide Modeling for Survival Prediction

Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining

Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image

Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging

Invariant Shape Representation Learning For Image Classification

Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning

Built with on top of