Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement

Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 78
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement | Route /signal-canvas/adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement",
    "query_text": "Summarize Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement",
  "normalized_query": "2603.28027",
  "route": "/signal-canvas/adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement",
  "paper_ref": "adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: 78

Proof: Verification pending

Freshness state: computing

Source paper: Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement

PDF: https://arxiv.org/pdf/2603.28027v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:20:53.176Z

Signal Canvas receipt window

Watch and verify: Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement

/buildability/adapting-sam-to-nuclei-instance-segmentation-and-classification-via-cooperative-fine-grained-refinement

Watchwatch

Subject: Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
dynamically aggregates multi-level encoder features to preserve fine-grained spatial details
Implicationpartial
The purpose and function of HMFM are clearly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Notably, on PanNuke, it outperforms the previous leading method (which required full fine-tuning) by 1.62% in bPQ and 1.34% in mPQ
Implicationpartial
Explicitly stated in the analysis with specific numeric metrics.
Verificationpartial
partial
Evidencepartial
while utilizing only 10.6% of the trainable parameters.
Implicationpartial
Explicitly stated in the analysis with a specific percentage.
Verificationpartial
partial
Evidencepartial
directly applying SAM to the medical imaging domain faces significant limitations: it lacks sufficient perception of the local structural features that are crucial for nuclei segmentation
Implicationpartial
Directly stated in the abstract as a core limitation of SAM in this domain.
Verificationpartial
partial
Evidencepartial
instilling a powerful perception of local structures through dynamically generated, multi-scale convolutional kernels
Implicationpartial
Claim about the method's mechanism is clearly described in the abstract and analysis.
Verificationpartial
partial
Evidencepartial
integrates multi-context boundary cues with semantic features through explicit supervision, producing a boundary-focused signal to refine initial mask predictions for sharper delineation.
Implicationpartial
The function and mechanism of BGMR are clearly stated in the abstract.
Verificationpartial
partial
Evidencepartial
SAM’s decoder ignores shallow features, and HMFM handles this at the fusion level.
Implicationpartial
Stated in the analysis as a specific shortcoming of SAM and the corresponding solution.
Verificationpartial
partial
Evidencepartial
CFR-SAM achieves end-to-end, fine-grained instance segmentation without relying on fragile post-processing.
Implicationpartial
Claim about the method's capability is stated in the analysis, though 'fragile' is a qualitative term.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement

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