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The dataset implements a columnar storage format (CSV-backed) with lazy loading semantics, enabling efficient streaming of large-scale medical imaging annotations without materializing the full dataset in memory. Supports pandas and polars backends for downstream processing.","intents":["Load a large-scale medical imaging dataset for model training without exhausting GPU memory","Access standardized whole-body CT segmentation annotations with consistent schema across 12M+ samples","Stream batches of medical images and corresponding segmentation masks for distributed training pipelines","Validate dataset integrity and schema compliance before training medical imaging models"],"best_for":["ML researchers training segmentation models on medical imaging data","Medical AI teams building whole-body CT analysis systems","Dataset curators validating large-scale medical imaging collections"],"limitations":["Fixed schema design — cannot dynamically add new modalities or annotation types without dataset regeneration","CSV-based storage introduces serialization overhead compared to binary formats like Parquet for large-scale streaming","3D volume data requires external storage references or chunked loading — not embedded in dataset records","No built-in data augmentation or preprocessing — requires separate pipeline for image normalization and spatial transforms","Medical imaging data subject to regulatory constraints (HIPAA, GDPR) — requires careful handling of patient privacy"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","pandas or polars for tabular processing","Internet connectivity for initial dataset download (~10GB+ estimated)","MLCroissant support for schema validation (optional but recommended)"],"input_types":["CSV metadata index","3D medical imaging volumes (CT format)","Segmentation mask annotations","Tabular patient/study metadata"],"output_types":["PyArrow Table (columnar in-memory format)","pandas DataFrame","polars DataFrame","Streaming batches of (image, segmentation_mask, metadata) tuples"],"categories":["data-processing-analysis","medical-imaging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mrmrx--cads-dataset__cap_1","uri":"capability://data.processing.analysis.schema.validated.medical.imaging.metadata.extraction.and.normalization","name":"schema-validated medical imaging metadata extraction and normalization","description":"Extracts and normalizes structured metadata (patient demographics, study parameters, segmentation labels) from raw medical imaging records using MLCroissant schema definitions. The dataset enforces type consistency, missing-value handling, and categorical standardization across 12M+ samples, enabling downstream models to rely on clean, validated feature representations without custom preprocessing. Metadata includes whole-body segmentation class hierarchies and imaging protocol parameters.","intents":["Extract normalized patient demographics and imaging parameters for cohort analysis","Validate segmentation label consistency across 12M+ medical imaging studies","Build feature engineering pipelines that assume clean, schema-compliant metadata","Generate summary statistics on dataset composition (age distribution, imaging protocols, label frequencies)"],"best_for":["Medical AI researchers requiring clean, validated metadata for model training","Clinical data scientists performing cohort analysis on large-scale imaging studies","Dataset curators ensuring data quality and consistency across multi-site medical imaging collections"],"limitations":["Schema is fixed at dataset creation time — cannot retroactively add new metadata fields without regenerating the dataset","Categorical standardization may lose domain-specific nuances (e.g., imaging protocol variations across institutions)","Missing-value imputation strategy is predetermined — cannot customize handling per use case","No support for temporal metadata (e.g., follow-up studies, longitudinal tracking) — treats each sample as independent","Metadata normalization assumes Western medical standards — may not generalize to non-English or non-DICOM imaging systems"],"requires":["MLCroissant library for schema parsing","Python 3.8+","pandas or polars for metadata manipulation","Understanding of medical imaging metadata standards (DICOM, NIfTI)"],"input_types":["Raw DICOM headers or NIfTI metadata","CSV metadata index with segmentation labels","Categorical imaging protocol descriptors"],"output_types":["Normalized pandas DataFrame with typed columns","Categorical feature vectors","Summary statistics (mean, std, frequency tables)","Validation reports (missing values, outliers, schema violations)"],"categories":["data-processing-analysis","medical-imaging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mrmrx--cads-dataset__cap_2","uri":"capability://data.processing.analysis.distributed.batch.sampling.for.medical.imaging.model.training","name":"distributed batch sampling for medical imaging model training","description":"Provides efficient batch sampling of medical imaging data (images, segmentation masks, metadata) using HuggingFace Datasets' distributed sampling primitives, enabling multi-GPU and multi-node training without data duplication or synchronization overhead. Supports stratified sampling by segmentation class or imaging protocol to ensure balanced batch composition. Integrates with PyTorch DataLoader for seamless training pipeline integration.","intents":["Sample balanced batches of medical images and segmentation masks for distributed training across multiple GPUs","Ensure each training node receives non-overlapping data samples without manual sharding logic","Stratify batches by segmentation class to handle class imbalance in medical imaging datasets","Stream data efficiently without loading entire dataset into memory on each node"],"best_for":["ML engineers training segmentation models on multi-GPU clusters","Research teams scaling medical imaging model training across distributed infrastructure","Teams building production medical imaging pipelines requiring reproducible, deterministic sampling"],"limitations":["Stratified sampling adds ~5-10% overhead per epoch due to class-aware shuffling logic","No built-in support for temporal or spatial locality — samples are shuffled independently, losing potential correlations","Batch composition is deterministic only if random seed is fixed — non-deterministic in distributed settings without careful synchronization","3D volume data requires external storage references — cannot efficiently batch large volumes without pre-chunking","No support for dynamic resampling based on model performance (e.g., hard example mining)"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","PyTorch (torch>=1.9.0) for DataLoader integration","Python 3.8+","Distributed training framework (PyTorch Lightning, Hugging Face Accelerate, or native torch.distributed)","Shared filesystem or object storage for multi-node access"],"input_types":["Medical imaging dataset loaded via HuggingFace Datasets","Segmentation class labels for stratification","Batch size and sampling strategy configuration"],"output_types":["PyTorch DataLoader yielding batches of (image, mask, metadata) tuples","Batches with shape (batch_size, channels, height, width, depth) for 3D volumes","Metadata tensors with patient demographics and imaging parameters"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mrmrx--cads-dataset__cap_3","uri":"capability://data.processing.analysis.multi.format.dataset.export.and.format.conversion","name":"multi-format dataset export and format conversion","description":"Exports medical imaging dataset to multiple downstream formats (CSV, Parquet, pandas DataFrame, polars DataFrame) using HuggingFace Datasets' format conversion primitives. Supports selective column export, compression options, and format-specific optimizations (e.g., Parquet columnar compression for analytics, CSV for human inspection). Enables seamless integration with downstream tools (pandas, polars, DuckDB, Spark) without custom serialization logic.","intents":["Export dataset to CSV for manual inspection and quality assurance","Convert to Parquet for efficient analytics and aggregation queries","Generate pandas or polars DataFrames for exploratory data analysis","Integrate dataset with downstream analytics tools (DuckDB, Spark, BigQuery)"],"best_for":["Data scientists performing exploratory analysis on medical imaging metadata","Analytics teams building dashboards and summary statistics","Teams integrating medical imaging datasets with existing data warehouses"],"limitations":["3D volume data cannot be efficiently embedded in CSV/Parquet — requires external storage references or chunking","Format conversion adds I/O overhead — exporting full 12M+ sample dataset may take hours","Compression options trade off speed vs. storage (e.g., Parquet snappy compression adds ~20% CPU overhead)","No built-in support for incremental exports — full dataset must be re-exported for updates","Metadata-only export loses image data — requires separate pipeline for volume data export"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","pandas (pandas>=1.0.0) for DataFrame export","polars (polars>=0.14.0) for polars export (optional)","pyarrow (pyarrow>=5.0.0) for Parquet support","Python 3.8+"],"input_types":["Medical imaging dataset loaded via HuggingFace Datasets","Column selection specification","Format and compression options"],"output_types":["CSV files with tabular metadata","Parquet files with columnar compression","pandas DataFrame in memory","polars DataFrame in memory","Compressed archives for bulk export"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mrmrx--cads-dataset__cap_4","uri":"capability://memory.knowledge.reproducible.dataset.versioning.and.citation.tracking","name":"reproducible dataset versioning and citation tracking","description":"Provides built-in versioning and citation metadata via HuggingFace Hub integration, enabling reproducible dataset access across research projects. Each dataset version is immutable and tagged with arXiv paper reference (2507.22953), enabling researchers to cite exact dataset versions in publications. Supports dataset snapshots, change tracking, and version-specific access patterns for long-term reproducibility.","intents":["Cite exact dataset version in research publications with arXiv reference","Access specific historical dataset versions for result reproduction","Track dataset changes and updates across research iterations","Ensure long-term reproducibility of medical imaging model training"],"best_for":["Academic researchers publishing medical imaging papers requiring reproducible datasets","Teams maintaining long-term medical imaging projects with evolving datasets","Institutions requiring audit trails and version history for medical data"],"limitations":["Version history is immutable — cannot retroactively modify or delete published versions","Dataset updates require publishing new versions — no in-place updates","HuggingFace Hub availability is required for version tracking — offline access requires local caching","Citation metadata is tied to HuggingFace Hub — may not integrate with all academic citation systems","No built-in support for dataset lineage tracking (e.g., which preprocessing steps generated this version)"],"requires":["HuggingFace Hub account and API access","Internet connectivity for version metadata retrieval","Python 3.8+","HuggingFace Datasets library (datasets>=2.0.0)"],"input_types":["Dataset version identifier (e.g., 'main', 'v1.0', commit hash)","Citation format specification (BibTeX, APA, etc.)"],"output_types":["Dataset snapshot at specific version","Citation metadata (BibTeX, DOI, arXiv reference)","Version history and changelog","Dataset card with documentation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mrmrx--cads-dataset__cap_5","uri":"capability://data.processing.analysis.whole.body.segmentation.class.hierarchy.and.label.standardization","name":"whole-body segmentation class hierarchy and label standardization","description":"Provides standardized segmentation class definitions and hierarchies for whole-body CT imaging, enabling consistent label interpretation across 12M+ samples. Implements class-to-ID mappings, hierarchical relationships (e.g., 'organs' → 'liver', 'kidney'), and class-specific metadata (e.g., typical HU ranges, anatomical constraints). Supports multi-label segmentation where samples may contain multiple organ annotations.","intents":["Map segmentation class names to numeric IDs for model training","Understand hierarchical relationships between anatomical structures","Handle multi-label segmentation where samples contain multiple organs","Validate segmentation masks against anatomical constraints (e.g., organs should not overlap)"],"best_for":["Medical imaging researchers training multi-class segmentation models","Clinical teams building organ-specific analysis pipelines","Dataset curators ensuring anatomical consistency in segmentation annotations"],"limitations":["Class hierarchy is fixed at dataset creation — cannot dynamically add new anatomical structures","Hierarchical relationships assume Western anatomical nomenclature — may not generalize to non-standard anatomies","No built-in support for partial segmentations (e.g., incomplete organ annotations) — requires custom handling","Class imbalance is inherent (e.g., small organs like pancreas are rare) — requires careful sampling strategy","Anatomical constraints are not enforced at load time — requires separate validation pipeline"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","Understanding of medical imaging segmentation standards (DICOM-SEG, NIfTI)"],"input_types":["Segmentation mask tensors (integer class IDs)","Class name strings","Hierarchical class relationship definitions"],"output_types":["Class-to-ID mapping dictionaries","Hierarchical class trees","Class-specific metadata (HU ranges, anatomical constraints)","Multi-label binary masks"],"categories":["data-processing-analysis","medical-imaging"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","pandas or polars for tabular processing","Internet connectivity for initial dataset download (~10GB+ estimated)","MLCroissant support for schema validation (optional but recommended)","MLCroissant library for schema parsing","pandas or polars for metadata manipulation","Understanding of medical imaging metadata standards (DICOM, NIfTI)","PyTorch (torch>=1.9.0) for DataLoader integration","Distributed training framework (PyTorch Lightning, Hugging Face Accelerate, or native torch.distributed)"],"failure_modes":["Fixed schema design — cannot dynamically add new modalities or annotation types without dataset regeneration","CSV-based storage introduces serialization overhead compared to binary formats like Parquet for large-scale streaming","3D volume data requires external storage references or chunked loading — not embedded in dataset records","No built-in data augmentation or preprocessing — requires separate pipeline for image normalization and spatial transforms","Medical imaging data subject to regulatory constraints (HIPAA, GDPR) — requires careful handling of patient privacy","Schema is fixed at dataset creation time — cannot retroactively add new metadata fields without regenerating the dataset","Categorical standardization may lose domain-specific nuances (e.g., imaging protocol variations across institutions)","Missing-value imputation strategy is predetermined — cannot customize handling per use case","No support for temporal metadata (e.g., follow-up studies, longitudinal tracking) — treats each sample as independent","Metadata normalization assumes Western medical standards — may not generalize to non-English or non-DICOM imaging systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mrmrx--cads-dataset","compare_url":"https://unfragile.ai/compare?artifact=mrmrx--cads-dataset"}},"signature":"WIMQrgyneCNOmoMPKImD79/smsk3NetdHFdRbCIdKl+lNLMx7iAddHTNosNAztts/+A4t6AuVN0elTZF4JJWBA==","signedAt":"2026-06-20T20:01:44.366Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mrmrx--cads-dataset","artifact":"https://unfragile.ai/mrmrx--cads-dataset","verify":"https://unfragile.ai/api/v1/verify?slug=mrmrx--cads-dataset","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}