ARXIV:2602.22545 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

arXiv

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability. We introduce DisQ-HNet (DQH), a framework that synthesizes tau-PET from paired T1-weighted and FLAIR MRI while exposing…

METHOD

Full abstract

Tau positron emission tomography (tau-PET) provides an in vivo marker of Alzheimer's disease pathology, but cost and limited availability motivate MRI-based alternatives. We introduce DisQ-HNet (DQH), a framework that synthesizes tau-PET from paired T1-weighted and FLAIR MRI while exposing how each modality contributes to the prediction. The method combines (i) a Partial Information Decomposition (PID)-guided, vector-quantized encoder that partitions latent information into redundant, unique, and complementary components, and (ii) a Half-UNet decoder that preserves anatomical detail using pseudo-skip connections conditioned on structural edge cues rather than direct encoder feature reuse. Across multiple baselines (VAE, VQ-VAE, and UNet), DisQ-HNet maintains reconstruction fidelity and better preserves disease-relevant signal for downstream AD tasks, including Braak staging, tau localization, and classification. PID-based Shapley analysis provides modality-specific attribution of synthesized uptake patterns.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. PID-based Shapley analysis provides modality-specific attribution of synthesized uptake patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 2.0/10.

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

Opportunity summary

Score2.0

PainDisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

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

ARXIV:2602.22545 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

arXiv

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. PID-based Shapley analysis provides modality-specific attribution of synthesized uptake patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 2.0/10.

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

Opportunity summary

Score2.0

PainDisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

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

Paper Pack

10.48550/arXiv.2602.22545

DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

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 fallback47 indexednot indexed

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

Viability

2.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. PID-based Shapley analysis provides modality-specific attribution of synthesized uptake patterns.

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 2.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability. We introduce DisQ-HNet (DQH), a framework that synthesizes tau-PET from paired T1-weighted and FLAIR MRI while exposing how each modality contributes to the prediction.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Tau positron emission tomography (tau-PET) provides an in vivo marker of Alzheimer's disease pathology, but cost and limited availability motivate MRI-based alternatives. We introduce DisQ-HNet (DQH), a framework that synthesizes tau-PET from paired T1-weighted and FLAIR MRI while exposing how each modality contributes to the prediction.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. PID-based Shapley analysis provides modality-specific attribution of synthesized uptake patterns.
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 2.0/10.
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

DisQ-HNet is a framework for synthesizing tau-PET images from MRI data to support Alzheimer's diagnostics with interpretability.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.22545 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(47)

2024 Alzheimer's disease facts and figures

2024

Deep learning based synthesis of MRI, CT and PET: Review and analysis

2023Sanuwani Dayarathna, Kh Tohidul Islam et al.

A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles

2023Samantha C Burnham, Leonardo Iaccarino et al.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning

2022Jeyeon Lee, B. Burkett et al.

Algorithms to estimate Shapley value feature attributions

2022Hugh Chen, Ian Covert et al.

Staging tau pathology with tau PET in Alzheimer’s disease: a longitudinal study

2021Shi‐Dong Chen, Jiaying Lu et al.

Disentanglement Analysis with Partial Information Decomposition

2021Seiya Tokui, Issei Sato

A partial information decomposition for discrete and continuous variables

2021Kyle Schick-Poland, Abdullah Makkeh et al.

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

2021Guang Yang, Qinghao Ye et al.

Cover

2020

Dementia prevention, intervention, and care: 2020 report of the Lancet Commission

2020G. Livingston, Jonathan Huntley et al.

OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease

2019P. LaMontagne, T. Benzinger et al.

A Novel Approach to the Partial Information Decomposition

2019Artemy Kolchinsky

Mish: A Self Regularized Non-Monotonic Neural Activation Function

2019Diganta Misra

Generating Diverse High-Fidelity Images with VQ-VAE-2

2019Ali Razavi, Aäron van den Oord et al.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

2019Mingxing Tan, Quoc V. Le

Semantic Image Synthesis With Spatially-Adaptive Normalization

2019Taesung Park, Ming-Yu Liu et al.

Transfusion: Understanding Transfer Learning for Medical Imaging

2019M. Raghu, Chiyuan Zhang et al.

NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

2018C. Jack, David A. Bennett et al.

Neural Discrete Representation Learning

2017Aäron van den Oord, O. Vinyals et al.

Showing 20 of 47 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 ViabilityModality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI

7.0

Higher ViabilityHQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation

7.0

Higher ViabilityAdaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation

8.0

Higher ViabilityInvestigating Permutation-Invariant Discrete Representation Learning for Spatially Aligned Images

4.0

Higher ViabilityMulti-modal Imputation for Alzheimer's Disease Classification

5.0

Higher ViabilityIntroductionDMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement

5.0

Higher ViabilityBridging MRI and PET physiology: Untangling complementarity through orthogonal representations

7.0

Higher ViabilityDECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising

7.0

Higher ViabilityMAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising

6.0

Higher ViabilityTranslating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness

8.0

Conflicting

none indexed

Related Resources

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Owned Distribution

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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: 2602.22545
Route: /paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe
Active tab: read
Artifact: disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe/paper-pack
REST build passport API/api/v1/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe/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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  "paper_id": "f303a709-6fdd-45f1-9de4-2f5effaddc2d",
  "arxiv_id": "2602.22545",
  "canonical_route": "/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe",
  "endpoints": {
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    "build_passport": "/api/v1/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

stale

Proof freshness: stale
Proof status: unverified
Display score: 2/10
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

DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

Canonical ID disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe | Route /paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.22545"
  }
}

source_context

{
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  "mode": "paper",
  "query": "DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI",
  "normalized_query": "2602.22545",
  "route": "/paper/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe",
  "paper_ref": "disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Not build-ready: DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

/buildability/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

Ignoreblocked

Subject: DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

Paper ref

disq-hnet-a-disentangled-quantized-half-unet-for-interpretable-multimodal-image-synthesis-applications-to-tau-pet-synthe

arXiv id

2602.22545

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

c172510baf11a747e6982beb4383d614af476ca824284f24a875cfad64d58993

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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DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

DisQ-HNet: A Disentangled Quantized Half-UNet for Interpretable Multimodal Image Synthesis Applications to Tau-PET Synthesis from T1 and FLAIR MRI

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