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Accelerating Data Processing and Benchmarking of AI Models for Pathology
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Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field.
Forward citations
Cited by 8 Pith papers
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Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer
SmartStu distills multiple teacher pathology models into compact breast-cancer encoders with an adversarial noise model and self-supervision, matching or improving external-cohort accuracy at over 30x smaller size.
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Zero-Cost Virtual RNA: Approximating Immunotherapy Signatures via Cross-Modal WSI Retrieval
VITA aligns H&E slides and RNA signatures in a shared latent space, then imputes a patient's RNA signature by retrieving morphologically similar historical cases, reaching 0.72 classification accuracy and 0.66 Spearma...
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MOOZY: A Patient-First Foundation Model for Computational Pathology
Patient-level pretraining with a case transformer and multi-task public supervision yields transferable WSI embeddings that beat larger slide-centric models on held-out pathology tasks.
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Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer
Multimodal fusion of CT and pathology images improves recurrence risk prediction in kidney cancer, with the best model approaching the clinical Leibovich score.
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Towards Comprehensive Cellular Characterisation of H&E slides
A compact pathology foundation model (H0-mini) integrated into the CellViT architecture, trained on a new pan-cancer 13-class nucleus dataset, matches larger models on detection and improves rare-cell classification o...
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Towards Robust Foundation Models for Digital Pathology
PathoROB shows that all 20 evaluated pathology foundation models encode medical center information and that lower robustness correlates with larger downstream performance drops.
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The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models
A label-free attack that perturbs only 0.1% of patches in a whole-slide image can shift the model's global representation and substantially degrade downstream pathology task accuracy.
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PathReportEval: A Systematic Benchmark for Pathology Report Generation
PathReportEval standardizes pathology report generation evaluation and introduces CRQS, a clinically grounded metric that better detects diagnostic errors than BLEU/ROUGE/METEOR.
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