ARXIV:2603.08328 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology

arXiv

Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods. Heatmaps are widely used to validate MIL models and to…

METHOD

Full abstract

Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large-scale benchmark experiment to assess six explanation methods across histopathology task types (classification, regression, survival), MIL model architectures (Attention-, Transformer-, Mamba-based), and patch encoder backbones (UNI2, Virchow2). Our results show that explanation quality mostly depends on MIL model architecture and task type, with perturbation ("Single"), layer-wise relevance propagation (LRP), and integrated gradients (IG) consistently outperforming attention-based and gradient-based saliency heatmaps, which often fail to reflect model decision mechanisms. We further demonstrate the advanced capabilities of the best-performing explanation methods: (i) We provide a proof-of-concept that MIL heatmaps of a bulk gene expression prediction model can be correlated with spatial transcriptomics for biological validation, and (ii) showcase the discovery of distinct model strategies for predicting human papillomavirus (HPV) infection from head and neck cancer slides. Our work highlights the importance of validating MIL heatmaps and establishes that improved explainability can enable more reliable model validation and yield biological insights, making a case for a broader adoption of explainable AI in digital pathology. Our code is provided in a public GitHub repository: https://github.com/bifold-pathomics/xMIL/tree/xmil-journal

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results show that explanation quality mostly depends on MIL model architecture and task type, with perturbation ("Single"), layer-wise relevance propagation (LRP), and integrated…

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

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Opportunity summary

Score7.0

PainImprove histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

Improve histopathology biomarker discovery by validating and improving the explainability of multiple instance learning models using a new benchmarking framework and advanced explanation methods.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology

Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology

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