ARXIV:2603.22198 · COMPUTATIONAL PATHOLOGY AI · SUBMITTED 24 MAR · 21:26 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: partial proof status

Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning

Daniel Shao · Joel Runevic · Richard J. Chen · Drew F. K. Williamson · Ahrong Kim · Andrew H. Song · +1 at arXiv

A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

Ship in 2-4 weeks›Score7.0Evidence partial

Opportunity summary

Pain A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

Evidence 0 refs | 0 sources | 50% coverage

Blocker Evidence partial

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PROBLEM

A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer…

METHOD

Full abstract

Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer to obtain task-specific patch features, and 3) aggregating the patches into a slide feature for classification. While substantial efforts have been devoted to optimizing patch feature extraction and aggregation, none have yet addressed the second point, the critical layer which transforms general-purpose features into task-specific features. We hypothesize that this layer constitutes an overlooked performance bottleneck and that stronger representations can be achieved with a low-rank transformation tailored to each patch's phenotype, yielding synergistic effects with any of the existing MIL approaches. To this end, we introduce MAMMOTH, a parameter-efficient, multi-head mixture of experts module designed to improve the performance of any MIL model with minimal alterations to the total number of parameters. Across eight MIL methods and 19 different classification tasks, we find that such task-specific transformation has a larger effect on performance than the choice of aggregation method. For instance, when equipped with MAMMOTH, even simple methods such as max or mean pooling attain higher average performance than any method with the standard linear layer. Overall, MAMMOTH improves performance in 130 of the 152 examined configurations, with an average $+3.8\%$ change in performance. Code is available at https://github.com/mahmoodlab/mammoth.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. To this end, we introduce MAMMOTH, a parameter-efficient, multi-head mixture of experts module designed to improve the performance of any MIL model with minimal…

WHY NOW

Computational Pathology AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

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Opportunity summary

Score7.0

PainA parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

Evidence0 refs | 0 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: partial proof status

Competitive landscape

A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

Segment

Computational Pathology AI

Adoption evidence

Public code linked for build inspection

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

A parameter-efficient module that significantly boosts the performance of computational pathology AI models by optimizing the transformation of patch features.

Segment

Computational Pathology AI

Adoption evidence

Public code linked for build inspection

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning

Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning

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