An Open Benchmark for IHC-driven Computational Pathology
A comprehensive benchmarking framework for evaluating foundation models across diverse immunohistochemistry tasks in precision oncology.
IHC Staining Assessment
Evaluating spatial patterns, intensity, subcellular location, and quantity.
Biomarker Expression
Predicting marker-specific protein signals across diverse heterogeneous tissue contexts.
Diagnosis & Grading
Robust recognition of histologic patterns across tumor subtypes and progression states.
Microenvironment
Fine-grained tissue composition classification and spatial context from IHC inputs.
Progression & Prognosis
Clinical risk stratification, survival analysis and time-to-event outcomes.
Therapeutic Response
Predicting treatment efficacy across multiple settings, including chemotherapy, targeted therapy, and immunotherapy.
(Lower is better)
(#1)
(count)
A Critical Blind Spot
Current models focus on H&E, missing crucial IHC signals needed for real-world clinical decisions. ImmunoBench establishes IHC-centered evaluation as a necessary dimension.
Task-Dependent Landscape
No single model dominates. While models excel at local IHC signals, complex clinical endpoints remain challenging, requiring better integration of spatial and molecular context.
An Open Ecosystem
More than a benchmark, ImmunoBench is a durable resource offering curated datasets, standardized protocols, and a dynamic leaderboard to drive future model development.
Performance Boundaries
Models excel at spatial patterns but struggle with sparse signals, outcome prediction, and cross-center generalization.
Architectural Impact
Patch-level models dominate local recognition. IHC-aware and multimodal pretraining provide selective, not universal, benefits.
Multi-Stain Integration
Simply combining H&E and IHC is insufficient. Ensembles reveal current models encode complementary but incomplete information.