ARXIV:2602.15811 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Task-Agnostic Continual Learning for Chest Radiograph Classification

Q: What products could be built from this research?

The product would be an adaptable plugin or API for existing radiology image processing systems, enabling ongoing updates and improved diagnostic accuracy as new data are acquired.

Q: What are the practical use cases?

CARL-XRay could be used in hospital systems worldwide to update diagnostic models as new chest radiograph data are collected, without disrupting ongoing operations or requiring vast storage for raw data.

Q: What industries could this research disrupt?

The system could replace existing static models that require periodic large-scale retraining, offering a more streamlined approach to model updates in dynamic clinical environments.

arXiv

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment. We study, for the first time, a task-incremental continual learning setting for chest radiograph classification, in which heterogeneous chest…

METHOD

Full abstract

Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previously ob- served data or degrading validated performance. We study, for the first time, a task-incremental continual learning setting for chest radiograph classification, in which heterogeneous chest X-ray datasets arrive sequentially and task identifiers are unavailable at inference. We propose a continual adapter-based routing learning strategy for Chest X-rays (CARL-XRay) that maintains a fixed high-capacity backbone and incrementally allocates lightweight task-specific adapters and classifier heads. A latent task selector operates on task-adapted features and leverages both current and historical context preserved through compact prototypes and feature-level experience replay. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage. Experiments on large-scale public chest radiograph datasets demonstrate robust performance retention and reliable task-aware inference under continual dataset ingestion. CARL-XRay outperforms joint training under task-unknown deployment, achieving higher routing accuracy (75.0\% vs.\ 62.5\%), while maintaining competitive diagnostic performance with AUROC of 0.74 in the oracle setting with ground-truth task identity and 0.75 under task-unknown inference, using significantly fewer trainable parameters. Finally, the proposed framework provides a practical alternative to joint training and repeated full retraining in continual clinical deployment.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage.

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

PainIntroducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

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

ARXIV:2602.15811 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Task-Agnostic Continual Learning for Chest Radiograph Classification

arXiv

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

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 5.0/10 on the public viability pass. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage.

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

PainIntroducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

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

Paper Pack

10.48550/arXiv.2602.15811

Task-Agnostic Continual Learning for Chest Radiograph Classification

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

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 fallback23 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

ScienceToStartup currently rates this 5.0/10 on the public viability pass. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage.

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
Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment. We study, for the first time, a task-incremental continual learning setting for chest radiograph classification, in which heterogeneous chest X-ray datasets arrive sequentially and task identifiers are unavailable at inference.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previously ob- served data or degrading validated performance. We study, for the first time, a task-incremental continual learning setting for chest radiograph classification, in which heterogeneous chest X-ray datasets arrive sequentially and task identifiers are unavailable at inference.
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. This design supports stable task identification and adaptation across sequential updates while avoiding raw-image storage.
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

Introducing CARL-XRay: an efficient continual learning framework optimizing chest radiograph classification for sequential clinical deployment.

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 2602.15811 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(23)

Nested Learning: The Illusion of Deep Learning Architectures

2025Ali Behrouz, Meisam Razaviyayn et al.

A fully open AI foundation model applied to chest radiography

2025Dongao Ma, Jiaxuan Pang et al.

Titans: Learning to Memorize at Test Time

2024Ali Behrouz, Peilin Zhong et al.

Freeze the Backbones: a Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-Training

2024Jiuming Qin, Che Liu et al.

From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification

2023Pranav Kulkarni, Adway U. Kanhere et al.

RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

2022P. Chambon, Christian Blüthgen et al.

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

2022Zifeng Wang, Zhenbang Wu et al.

Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

2022E. Tiu, Ellie Talius et al.

The Clinician and Dataset Shift in Artificial Intelligence.

2021S. G. Finlayson, Adarsh Subbaswamy et al.

Big Self-Supervised Models Advance Medical Image Classification

2021Shekoofeh Azizi, Basil Mustafa et al.

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

2021Ze Liu, Yutong Lin et al.

Contrastive Learning of Medical Visual Representations from Paired Images and Text

2020Yuhao Zhang, Hang Jiang et al.

MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

2019Alistair E. W. Johnson, T. Pollard et al.

A Continual Learning Survey: Defying Forgetting in Classification Tasks

2019Matthias De Lange, Rahaf Aljundi et al.

Parameter-Efficient Transfer Learning for NLP

2019N. Houlsby, A. Giurgiu et al.

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

2019J. Irvin, Pranav Rajpurkar et al.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

2018J. Zech, Marcus A. Badgeley et al.

Continual Lifelong Learning with Neural Networks: A Review

2018G. I. Parisi, Ronald Kemker et al.

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

2017Pranav Rajpurkar, J. Irvin et al.

Gradient Episodic Memory for Continual Learning

2017David Lopez-Paz, Marc'Aurelio Ranzato

Showing 20 of 23 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 ViabilityClinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models

7.0

Higher ViabilityEfficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning

7.0

Higher ViabilityTemporal Inversion for Learning Interval Change in Chest X-Rays

7.0

Higher ViabilityProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification

8.0

Higher ViabilitySeeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays

7.0

Higher ViabilityTowards Fair and Robust Volumetric CT Classification via KL-Regularised Group Distributionally Robust Optimisation

7.0

Higher ViabilityXrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis

7.0

Higher ViabilityBi-CRCL: Bidirectional Conservative-Radical Complementary Learning with Pre-trained Foundation Models for Class-incremental Medical Image Analysis

7.0

Higher ViabilityFollowing the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis

7.0

Conflicting

Competing ApproachCXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification

4.0

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: 2602.15811
Route: /paper/task-agnostic-continual-learning-for-chest-radiograph-classification
Active tab: read
Artifact: task-agnostic-continual-learning-for-chest-radiograph-classification

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/task-agnostic-continual-learning-for-chest-radiograph-classification/paper-pack
REST build passport API/api/v1/paper/task-agnostic-continual-learning-for-chest-radiograph-classification/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/task-agnostic-continual-learning-for-chest-radiograph-classification

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

Task-Agnostic Continual Learning for Chest Radiograph Classification

Canonical ID task-agnostic-continual-learning-for-chest-radiograph-classification | Route /paper/task-agnostic-continual-learning-for-chest-radiograph-classification

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/task-agnostic-continual-learning-for-chest-radiograph-classification

MCP example

{
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  }
}

source_context

{
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  "mode": "paper",
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  "benchmark_ref": null,
  "dataset_ref": null
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Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Task-Agnostic Continual Learning for Chest Radiograph Classification

/buildability/task-agnostic-continual-learning-for-chest-radiograph-classification

Watchwatch

Subject: Task-Agnostic Continual Learning for Chest Radiograph Classification

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/task-agnostic-continual-learning-for-chest-radiograph-classification

Paper ref

task-agnostic-continual-learning-for-chest-radiograph-classification

arXiv id

2602.15811

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

617506cbd6f448d66cadbe5f53345227442dd7ee68460166d260ef2192045f3c

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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Task-Agnostic Continual Learning for Chest Radiograph Classification

Task-Agnostic Continual Learning for Chest Radiograph Classification

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