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radiology-reader

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更新时间2026年7月11日 16:27

Build full-paper Chinese-English side-by-side (中英对照), figure/table-aware, source-grounded Markdown readers for imaging-research papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read an imaging paper, make 中英文对照/原文对照/全文翻译解读, extract figures/tables (including imaging panels and result charts) into the right positions, or keep exact source anchors. Especially attentive to radiology specifics: imaging figures, ROC/forest/KM charts, radiomics/AI methods detail, scanner/protocol tables, and statistical reporting. Must not degrade into a summary-only output unless the user explicitly asks for a summary.

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huang-sir1

huang-sir1/radiology-skills

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在 Manus 中运行

Full-Paper Markdown Reader (imaging-tuned)

Turn an imaging-research paper into a complete, bilingual, source-grounded Markdown reading artifact. The default output is a paragraph-level 中英对照 companion — not a summary.

Non-negotiable defaults

When the user asks to read/translate a paper, or says 中英对照 / 原文对照 / 全文翻译 / paper reader, produce a paragraph-level bilingual reader by default. Do not replace it with a Chinese-only summary, a highlights list, or captions without figure crops.

What to preserve (and why it matters for imaging papers)

Full prose, paragraph structure, and section flow (incl. Materials and Methods detail — scanner/protocol, segmentation, model/feature pipeline, statistical analysis).
Original + faithful Chinese translation at block level; keep technical terms, gene/model names, units, p-values, CIs, and citation markers intact.
Figures and tables placed near their first substantive mention. Crop tightly:
- Imaging panels — keep windowing/arrow annotations visible; note the modality/sequence.
- Result charts (ROC, calibration, forest, Kaplan-Meier, DCA) — keep axes/legend legible.
- Tables (cohort characteristics, scanner parameters, performance) — keep near the interpreting paragraph.
Stable anchors on every block (S### body, C### captions, F### figures, T### tables).

When to open extra files

File	Open when
references/structured-reading-notes.md	The user is reading for literature review, paper comparison, journal club, gap discovery, manuscript writing, or wants craft/figure/limitation patterns rather than translation only

Workflow

Identify source & paper type (DTA / prediction model / radiomics / radiogenomics / review) — this sets how tightly to couple text, figures, and stats.
Build a full source map before translating (page, block type, original, translation, reading order, nearby figure/table, confidence). Process the whole document, not just the abstract.
Translate conservatively — meaning not style; keep Methods/stats detail; mark uncertain OCR rather than guessing; never drop limitations / data-availability / ethics.
Extract & place figures/tables at first substantive mention; tight crops; keep caption
- Chinese caption; add a one-line reading note (what to inspect — e.g. "AUC and CI in panel A; calibration in panel B").
For literature-review or journal-club reading, open structured-reading-notes.md and create Pass 1/2/3 notes at the depth the task deserves.
Generate paper.md (primary) + source_map.json + translation_notes.md + assets/. Add a terminology table for recurring imaging/AI terms.
Answer follow-ups from the source with block IDs + page numbers; don't answer from memory.

Block shapes

<a id="S001"></a>
**Source:** p.1 S001
**Original:** [source paragraph]
**中文:** [faithful translation]

<a id="F001"></a>
### Fig 1. [short translated title]
**Placed near:** p.3 S012  **Source:** p.4 C001
![Fig 1](assets/fig1.png)
**Original caption:** [...]
**中文图注:** [...]
**Reading note:** [what to inspect — e.g. modality/window; which metric/CI]

Output contract

paper.md with **Original:**/**中文:** pairs for all substantive blocks.
Every figure/table in assets/ has a Markdown block + source pointer; every link resolves.
source_map.json parses; translation_notes.md records skipped/uncertain/draft content.
If structured reading was requested: paper note with depth level, claim-evidence table, and craft/positioning notes where relevant.
Don't hide missing content — label draft mode.

Tooling & handoffs

PDF extraction/OCR → load the pdf skill first.
Citation export of the paper's references → radiology-citation.
Want a journal-club deck instead → radiology-paper2ppt.
Quality bar: feels like a paper reader, not a machine-translation dump; reader can move between original ↔ translation ↔ source location ↔ figure/table evidence.

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2026-07-11706

radiology-citation

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Turn manuscript text, claims, figure/table statements, abstracts, slides, or novelty/comparison assertions into verified, imaging-journal-scoped citation candidates and export one reference-manager-ready file (RIS, EndNote ENW, or BibTeX). Use when the user needs references for an imaging paper, wants supporting citations, wants a two-pass claim/citation/numerical verification gate, needs to check whether a cited source actually says the claimed thing, scope citations to radiology/imaging journals (Radiology, Radiology: AI, RadioGraphics, AJR, European Radiology, JACR, etc.), verify a DOI/PMID, or export a bibliography. Verifies identifiers before formatting and never fabricates DOIs, pages, volumes, journal metadata, or source support.

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radiology-data

huang-sir1/radiology-skills

Prepare and audit Data/Code Availability statements, DICOM de-identification plans, repository selection, dataset citations, and FAIR/sharing checks for Radiology (RSNA) and Nature-portfolio imaging+omics submissions. Use when the user needs a data availability statement, must de-identify DICOM imaging, choose a repository (TCIA, Zenodo, GEO, dbGaP, EGA), share code/models, write dataset citations, check FAIR compliance, or needs Extended Data / Supplementary Information / Source Data planning for a Nature-family journal — including controlled-access genomics for radiogenomics. Bilingual-aware (中文作者备注 → submission-ready English). Never overstates availability or invents accessions.

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radiology-deep-learning

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Design and audit imaging deep-learning studies to Radiology (RSNA) / CLAIM 2024 standard, or to Nature-portfolio / FUTURE-AI trustworthy-AI standard — architecture choice (2D/2.5D/3D CNN, Transformer/ViT, segmentation/detection nets, prognostic models), transfer learning vs self-supervised pretraining vs training from scratch, how images/masks/clinical/text/molecular inputs enter the model, data splitting and augmentation, class imbalance, hyperparameter search, baselines, external validation, interpretability/explainability (Grad-CAM, SHAP, attention), uncertainty quantification (MC dropout, ensembles, conformal prediction), and robustness/OOD testing — with patient-level partition hygiene throughout. Use when the user plans or reviews a CNN/Transformer/3D/segmentation/detection/foundation/multimodal imaging model, mentions transfer learning, self-supervised, nnU-Net, ViT, data augmentation, class imbalance, explainability, uncertainty, robustness, or "影像深度学习/深度学习模型". Produces a model+training+validation des

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radiology-design

huang-sir1/radiology-skills

Assess whether an imaging dataset can support a study and turn it into a complete, submittable design — from feasibility triage to clinical question, target population, endpoint/estimand, minimum-viable vs stronger methods, and a validation strategy (internal resampling, temporal, geographic, fully external, multi-center, federated). Use when the user has CT/MRI/PET/US/mammography/multimodal data but is unsure what to do, asks "can this topic be done?" / "能不能做" / "帮我设计课题" / "study design" / "what can I study with this data", or needs a multi-center / external-validation plan ("多中心", "external validation", "generalisability", "center effect", "scanner effect"). Produces a study blueprint with feasibility verdict, design options, validation plan, and the limiting constraint surfaced. Never invents cohort numbers or overstates what the data can support.

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radiology-ethics

huang-sir1/radiology-skills

Write compliant, non-overstated Ethics, Informed-Consent, and privacy/governance text for imaging and imaging+omics studies — study-type framing (retrospective, prospective, registered, multi-center), IRB/ethics-committee approval and numbers, consent vs documented waiver, re-identification risk (DICOM metadata, rare-disease small cohorts, genomic data), and the human-subjects governance behind data sharing (HIPAA/GDPR/PIPL, data-use agreements, controlled access). Use when the user mentions ethics, IRB, ethics committee, informed consent, consent waiver, "伦理", "知情同意", "隐私", HIPAA, GDPR, PIPL, data-use agreement, or needs the Ethics/Consent statement for a manuscript or grant. Produces submission-ready statements and a re-identification risk read. Never fabricates approval numbers, dates, or consent status.

2026-07-11706

name

radiology-reader

description

Full-Paper Markdown Reader (imaging-tuned)

Turn an imaging-research paper into a complete, bilingual, source-grounded Markdown reading artifact. The default output is a paragraph-level 中英对照 companion — not a summary.

Non-negotiable defaults

What to preserve (and why it matters for imaging papers)

Full prose, paragraph structure, and section flow (incl. Materials and Methods detail — scanner/protocol, segmentation, model/feature pipeline, statistical analysis).
Original + faithful Chinese translation at block level; keep technical terms, gene/model names, units, p-values, CIs, and citation markers intact.
Figures and tables placed near their first substantive mention. Crop tightly:
- Imaging panels — keep windowing/arrow annotations visible; note the modality/sequence.
- Result charts (ROC, calibration, forest, Kaplan-Meier, DCA) — keep axes/legend legible.
- Tables (cohort characteristics, scanner parameters, performance) — keep near the interpreting paragraph.
Stable anchors on every block (S### body, C### captions, F### figures, T### tables).

When to open extra files

File	Open when
references/structured-reading-notes.md	The user is reading for literature review, paper comparison, journal club, gap discovery, manuscript writing, or wants craft/figure/limitation patterns rather than translation only

Workflow

Identify source & paper type (DTA / prediction model / radiomics / radiogenomics / review) — this sets how tightly to couple text, figures, and stats.
Build a full source map before translating (page, block type, original, translation, reading order, nearby figure/table, confidence). Process the whole document, not just the abstract.
Translate conservatively — meaning not style; keep Methods/stats detail; mark uncertain OCR rather than guessing; never drop limitations / data-availability / ethics.
Extract & place figures/tables at first substantive mention; tight crops; keep caption
- Chinese caption; add a one-line reading note (what to inspect — e.g. "AUC and CI in panel A; calibration in panel B").
For literature-review or journal-club reading, open structured-reading-notes.md and create Pass 1/2/3 notes at the depth the task deserves.
Generate paper.md (primary) + source_map.json + translation_notes.md + assets/. Add a terminology table for recurring imaging/AI terms.
Answer follow-ups from the source with block IDs + page numbers; don't answer from memory.

Block shapes

<a id="S001"></a>
**Source:** p.1 S001
**Original:** [source paragraph]
**中文:** [faithful translation]

<a id="F001"></a>
### Fig 1. [short translated title]
**Placed near:** p.3 S012  **Source:** p.4 C001
![Fig 1](assets/fig1.png)
**Original caption:** [...]
**中文图注:** [...]
**Reading note:** [what to inspect — e.g. modality/window; which metric/CI]

Output contract

paper.md with **Original:**/**中文:** pairs for all substantive blocks.
Every figure/table in assets/ has a Markdown block + source pointer; every link resolves.
source_map.json parses; translation_notes.md records skipped/uncertain/draft content.
If structured reading was requested: paper note with depth level, claim-evidence table, and craft/positioning notes where relevant.
Don't hide missing content — label draft mode.

Tooling & handoffs

PDF extraction/OCR → load the pdf skill first.
Citation export of the paper's references → radiology-citation.
Want a journal-club deck instead → radiology-paper2ppt.
Quality bar: feels like a paper reader, not a machine-translation dump; reader can move between original ↔ translation ↔ source location ↔ figure/table evidence.