ARXIV:2602.22955 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

arXiv

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI…

METHOD

Full abstract

Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing public datasets remain limited in annotation richness and diagnostic semantics. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities. To mitigate the scarcity and high cost of diagnostic semantic annotations, we develop a multi-model collaborative pipeline for automated medical information completion and quality control, enabling the generation of diagnosis-related semantics beyond mask-only annotations. Building upon this dataset, we further construct MM-NeuroOnco-Bench, a manually annotated evaluation benchmark with a rejection-aware setting to reduce biases inherent in closed-ended question formats. Evaluation across ten representative models shows that even the strongest baseline, Gemini 3 Flash, achieves only 41.88% accuracy on diagnosis-related questions, highlighting the substantial challenges of multimodal brain tumor diagnostic understanding. Leveraging MM-NeuroOnco, we further propose NeuroOnco-GPT, which achieves a 27% absolute accuracy improvement on diagnostic questions following fine-tuning. This result demonstrates the effectiveness of our dataset and benchmark in advancing clinically grounded multimodal diagnostic reasoning. Code and dataset are publicly available at: https://github.com/gfnnnb/MM-NeuroOnco

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Evaluation across ten representative models shows that even the strongest baseline, Gemini 3 Flash, achieves only 41.88% accuracy on diagnosis-related questions, highlighting the substantial…

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainA benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

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

ARXIV:2602.22955 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

arXiv

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainA benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

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

Paper Pack

10.48550/arXiv.2602.22955

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback33 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing public datasets remain limited in annotation richness and diagnostic semantics. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Evaluation across ten representative models shows that even the strongest baseline, Gemini 3 Flash, achieves only 41.88% accuracy on diagnosis-related questions, highlighting the substantial challenges of multimodal brain tumor diagnostic understanding.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Medical AI moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Medical AI

Competitors

not indexed

Competitive landscape

A benchmark and dataset for enhancing MRI-based brain tumor diagnosis models with clinically grounded multimodal reasoning.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.22955 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(33)

Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding

2025Songtao Jiang, Yuan Wang et al.

DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice

2025Zijie Meng, Jin Hao et al.

V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis

2025Yuan Wang, Jiaxiang Liu et al.

3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks

2025Xiaotang Gai, Jiaxiang Liu et al.

Qwen2.5-VL Technical Report

2025Shuai Bai, Keqin Chen et al.

A review of deep learning for brain tumor analysis in MRI

2025Felix J. Dorfner, Jay B. Patel et al.

MedCoT: Medical Chain of Thought via Hierarchical Expert

2024Jiaxiang Liu, Yuan Wang et al.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

2024Junlong Cheng, Bin Fu et al.

Foundation models for fast, label-free detection of glioma infiltration

2024A. Kondepudi, M. Pekmezci et al.

CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2017-2021.

2024M. Price, C. Ballard et al.

M4CXR: Exploring Multitask Potentials of Multimodal Large Language Models for Chest X-Ray Interpretation

2024Jonggwon Park, Soobum Kim et al.

GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI

2024Pengcheng Chen, Jin Ye et al.

MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

2024Yunfei Xie, Ce Zhou et al.

GREEN: Generative Radiology Report Evaluation and Error Notation

2024Sophie Ostmeier, Justin Xu et al.

MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale

2024Xiaotang Gai, Chenyi Zhou et al.

Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

2024F. Bray, M. Laversanne et al.

MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation

2023Zexue He, Yu Wang et al.

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

2023Chunyuan Li, Cliff Wong et al.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

2023Maruf Adewole, J. Rudie et al.

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

2021U. Baid, S. Ghodasara et al.

Showing 20 of 33 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

none indexed

Commercially relevant

Higher ViabilityNeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders

7.0

Higher ViabilityVirtual Biopsy for Intracranial Tumors Diagnosis on MRI

8.0

Higher ViabilityThe MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction

6.0

Higher ViabilityA Unified Multimodal Framework for Dataset Construction and Model-Based Diagnosis of Ameloblastoma

7.0

Higher ViabilityBrain-WM: Brain Glioblastoma World Model

8.0

Higher ViabilityA Novel Global Context-aware Deep Neural Network for Enhanced Brain Tumor Segmentation using Magnetic Resonance Images

7.0

Higher ViabilityNeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding

7.0

Higher ViabilityXMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

8.0

Conflicting

Competing ApproachCT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography

5.0

Competing ApproachMeta-D: Metadata-Aware Architectures for Brain Tumor Analysis and Missing-Modality Segmentation

3.0

Related Resources

What advancements are being made in medical AI?(question)
What advancements are being made in medical AI?(question)
Why is evidence-grounded reasoning important in medical AI?(question)
Medical AI – Use Cases(use_case)

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.22955
Route: /paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis
Active tab: read
Artifact: mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis/paper-pack
REST build passport API/api/v1/paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

Canonical ID mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis | Route /paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

MCP example

{
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  }
}

source_context

{
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}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

/buildability/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

Watchwatch

Subject: MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

Verdict

Watch

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

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

Paper ref

mm-neuroonco-a-multimodal-benchmark-and-instruction-dataset-for-mri-based-brain-tumor-diagnosis

arXiv id

2602.22955

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

3975b0b51f42099f886355c85be485d9ba6b4778d6b45f6c4880266ccaa3b97a

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

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