Stories about Foundation model
2 related stories
Morphology signal in whole slide image foundation models can automatically triage slides
AI InsightThis paper applies foundation models to automatic WSI triage, indicating that the bottleneck in AI pathology is shifting from model capability to data curation efficiency. By leveraging morphology signals, foundation models may partially replace manual annotation, though clinical deployment still requires robustness validation.Key TakeawayPathology AI research is shifting from manual slide selection to foundation-model-based automatic triage.Why It MattersSlide triage consumes significant expert time and data quality directly impacts model training. Reliable automatic triage could reduce annotation costs, improve downstream efficiency, and push forward standardized digital pathology workflows.Who's Affected- PathologistsAutomatic triage could reduce manual workload in selecting tumor-containing slides.
- AI Pathology ResearchersProvides a reusable pipeline for multi-slide datasets and improves training data quality.
- Diagnostic CentersRequires accuracy and generalization validation before clinical adoption; no near-term workflow replacement.
What's NextWatch for validation of this pipeline on real-world non-public datasets and the emergence of standardized benchmarks for multi-slide pathology data.Importance 60/100FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation
AI InsightThis study combines foundation model features with hard-negative mining for segmenting sparse functional tissue units in pathology images, suggesting that choosing what counts as a negative sample can matter as much as designing network architecture.Key TakeawayThe competitive focus in sparse pathology segmentation is shifting from network architectures to foundation model features and hard-negative strategies.Why It MattersSparse target structures in whole-slide pathology images create a bottleneck for automated quantification. This approach offers a feasible paradigm leveraging foundation model priors and negative-sample learning that could reduce manual annotation and downstream error costs if proven effective.Who's Affected- Pathology AI ResearchersGain a new baseline that combines foundation model features with hard-negative mining.
- Digital Pathology Tool VendorsIf successful, the method could improve automated detection of sparse structures such as TLSs and vessels.
What's NextWatch for benchmark comparisons on public pathology segmentation datasets and whether the negative-patch selection module generalizes across organs and staining conditions.Importance 52/100