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bids
Organizes, queries, validates, and converts Brain Imaging Data Structure (BIDS) datasets. Supports organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS deri…
histolab
Extracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.
imaging-data-commons
Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Goog…
nwb-conversion
Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped…
pyhealth
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM…
extracting-dicom-metadata
Reads DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and embedded report text, and flags PHI carried in header tags. Use before OpenMed processing when ingesting imaging data (CT/MR/CR/US, radiology SR) and you need the report narrative de-identified and analyzed, plus a list of header tags that must be scrubbed. H…
histolab
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
dicom-conformance-validator
DICOM conformance testing and integration skill for medical imaging systems
surface-analysis
Skill for surface composition, chemical state, and topography analysis including XPS depth profiling, AFM imaging, and contact angle measurements
stm-analyzer
Scanning Tunneling Microscopy skill for atomic-resolution imaging and local density of states measurements
tof-sims-analyzer
Time-of-Flight Secondary Ion Mass Spectrometry skill for molecular surface analysis and imaging
zemax-optical-designer
Zemax optical design skill for lens systems, imaging optics, and tolerancing analysis
tooluniverse-image-analysis
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay q…
tooluniverse-image-analysis
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay q…
tooluniverse-medical-imaging-radiology
Discover and characterize cancer radiology imaging cohorts from The Cancer Imaging Archive (TCIA) — list public collections (e.g. LIDC-IDRI, TCGA-GBM, TCGA-LUAD, TCGA-BRCA), enumerate patients/subjects and studies within a collection, browse DICOM series by modality (CT, MR, PT, CR, DX, US, NM) or body part (CHEST, BRAIN, ABDOMEN, ...), pull series-level DI…
tooluniverse-image-analysis
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay q…
tooluniverse-medical-imaging-radiology
Discover and characterize cancer radiology imaging cohorts from The Cancer Imaging Archive (TCIA) — list public collections (e.g. LIDC-IDRI, TCGA-GBM, TCGA-LUAD, TCGA-BRCA), enumerate patients/subjects and studies within a collection, browse DICOM series by modality (CT, MR, PT, CR, DX, US, NM) or body part (CHEST, BRAIN, ABDOMEN, ...), pull series-level DI…
An independently imported catalog with original authors, licenses and source links. Classification is not a security audit.
Sources and import coverage