Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
📰 ArXiv cs.AI
Learn to improve weakly-supervised pathology localization using cellular sheaves and classifier attention for more interpretable results
Action Steps
- Apply cellular sheaves to whole-slide images to capture local tissue structure
- Implement attention-based multiple instance learning (ABMIL) on top of foundation features for weakly-supervised classification
- Align classifier attention with cellular sheaves to improve localization accuracy
- Evaluate the performance of the model using metrics such as slide-level accuracy and localization precision
- Refine the model by adjusting parameters and exploring different attention mechanisms
Who Needs to Know This
This technique benefits researchers and engineers working on medical image analysis, particularly those focusing on pathology localization, as it enhances the interpretability of weakly-supervised models
Key Insight
💡 Cellular sheaves can be used to improve the interpretability of weakly-supervised pathology localization models by providing a more accurate localization signal
Share This
Enhance pathology localization with cellular sheaves and classifier attention #medicalimaging #pathology
Key Takeaways
Learn to improve weakly-supervised pathology localization using cellular sheaves and classifier attention for more interpretable results
Full Article
Title: Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
Abstract:
arXiv:2606.00092v1 Announce Type: cross Abstract: Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corresponding attention maps are an imperfect localization signal: in clinical interpretation, a model that classifies correctly without firing on the actual lesion is hard to trust. We address this gap with cellular sheaves, which eq
Abstract:
arXiv:2606.00092v1 Announce Type: cross Abstract: Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corresponding attention maps are an imperfect localization signal: in clinical interpretation, a model that classifies correctly without firing on the actual lesion is hard to trust. We address this gap with cellular sheaves, which eq
DeepCamp AI