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Accelerating Data Processing and Benchmarking of AI Models for Pathology
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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 14 Pith papers
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Benchmarking Pathology Foundation Models for Spatial Domain Understanding
SpaPath-Bench evaluates spatial representation in 19 pathology foundation models via spatial domain identification on 42 paired WSI-ST slides using three agreement criteria across 83K runs.
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Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
A new open pipeline and dataset enable training of a vision-language model for whole-slide pathology VQA that outperforms MedGemma on tissue identification, neoplasm detection, and differential diagnosis.
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How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology
Systematic factorial analysis shows optimized LLM input configurations for pathology WSIs raise GPT-5 performance from 15.1% to 39.5% on TCGA cancer classification and 38.1% to 62.9% on GTEx organ classification, with...
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Pathway-Structured Privileged Distillation for Deployable Computational Pathology
MoPE is a privileged distillation framework that transfers RNA-derived pathway supervision to histology experts via memory-usage alignment, improving whole-slide image only inference on cancer benchmarks.
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Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
An expert-guided contrastive fine-tuning framework improves fine-grained slide-level classification of pediatric brain tumors under low-data and class-imbalanced conditions by regularizing representations with clinica...
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Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
ASTRA unifies heterogeneous pathology foundation-model representations for pan-cancer classification and weakly supervised tumor localization using only slide-level structured annotations.
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PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
PC-MIL shows that anchoring supervision at a 2 mm scale and progressively mixing slide- and region-level labels improves cross-context accuracy in WSI cancer detection without reducing global performance.
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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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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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Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
DICE ensembles frozen pathology foundation models, aligns them with deep mutual learning to make disagreement a reliable uncertainty proxy, and shows consensus-based localization on WSI tasks.
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Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
MixTIME uses a learnable-router MoE to fuse three pathology foundation models for pixel- and slide-level prediction of 17 mIF protein markers from H&E images, improving spatial domain ID, survival prediction, and path...
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Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
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Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
An expert-guided contrastive fine-tuning framework improves fine-grained slide-level classification of pediatric brain tumors under low-sample and class-imbalanced conditions.
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Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.
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