MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification | Signal Canvas | ScienceToStartup

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MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification

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Proof freshness: stale
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Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification

Canonical ID mil-pf-multiple-instance-learning-on-precomputed-features-for-mammography-classification | Route /signal-canvas/mil-pf-multiple-instance-learning-on-precomputed-features-for-mammography-classification

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MCP example

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source_context

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
MIL-PF achieves state-of-the-art classification performance at clinical scale
Implicationpartial
Directly stated in abstract with strong performance claim
Verificationpartial
partial
Evidencepartial
substantially reducing training complexity
Implicationpartial
Directly stated in abstract with clear comparison to alternative methods
Verificationpartial
partial
Evidencepartial
training only a small task-specific aggregation module (40k parameters)
Implicationpartial
Specific numeric parameter count provided in abstract
Verificationpartial
partial
Evidencepartial
enables efficient experimentation and adaptation without retraining large backbones
Implicationpartial
Directly stated benefit of the approach in abstract
Verificationpartial
partial
Evidencepartial
explicitly models the global tissue context and the sparse local lesion signals through attention-based aggregation
Implicationpartial
Directly stated architectural feature in abstract
Verificationpartial
partial
Evidencepartial
making end-to-end fine-tuning computationally expensive and often impractical
Implicationpartial
Directly stated limitation of existing approaches in abstract
Verificationpartial
partial
Evidencepartial
Mammography, in particular, is characterized by large images, variable multi-view studies and predominantly breast-level labels
Implicationpartial
Directly stated domain-specific challenges in abstract
Verificationpartial
partial
Evidencepartial
We release the code for full reproducibility
Implicationpartial
Explicit statement about code availability in abstract
Verificationpartial
partial