Results for “medical-imaging”

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Access large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

Scans a DICOM series folder to extract header metadata and produce a preflight verdict without decoding pixel data.

Runs NV-Segment-CTMR segmentation on CT or MRI NIfTI volumes and records label-map evidence.

Extracts selected metadata from a DICOM file and flags standard-tag PHI presence. Not for anonymization or clinical use.

Process and manipulate images using ImageMagick: resize, convert formats, batch process, and retrieve metadata.

This skill recommends performing an imaging study to assess tumor size, appearance, and parasellar extent once biochemical diagnosis of acromegaly is confirmed. Trigger when IGF-1 is elevated and GH fails to suppress to <0.4 µg/L during an oral glucose tolerance test.

Create patient intake forms with history and screening. TRIGGERS - Use when user needs help with intake-form related tasks.

Explains biomedical concepts, vital signs, biological signals, medical device classifications, and healthcare data standards for technical teams.

Organize, query, validate, and convert neuroscience datasets following the Brain Imaging Data Structure (BIDS) standard, including metadata sidecars and derivatives.

Create patient intake forms with history and screening. TRIGGERS - Use when user needs help with intake-form related tasks.

Converts a single CT DICOM series folder into a Hounsfield Unit NIfTI volume with affine metadata.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: 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 derivatives.

Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.

Discovers and uses scientific datasets, models, blog posts, and interactive demos from a curated catalog for AI/ML work in domains like biology, chemistry, physics, and genomics.

Biblioteca Python para trabalhar com arquivos DICOM (Digital Imaging and Communications in Medicine). Use essa skill ao ler, escrever ou modificar dados de imagens médicas em formato DICOM, extrair dados de pixel de imagens médicas (TC, RM, Raio-X, ultrassom), anonimizar arquivos DICOM, trabalhar com metadados e tags DICOM, converter imagens DICOM para outros formatos, processar dados DICOM comprimidos ou processar conjuntos de dados de imagens médicas. Aplica-se a tarefas envolvendo análise de imagens médicas, sistemas PACS, fluxos de trabalho de radiologia e aplicações de imagem médica.

Stitched Image Quality Control

ISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use for internal audit planning, audit execution, finding classification, external audit preparation, or audit program management.

Identifies a medication from a photo and generates a genotype-informed dosage card using CPIC guidelines and real 23andMe data.

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Pixtral 12B: A Frontier Multimodal Model

Segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks with zero-shot transfer.

Kit de ferramentas de patologia computacional para análise de imagens de lâminas inteiras (WSI) e dados de imagem multiparamétrica. Use esta habilidade ao trabalhar com lâminas de histopatologia, imagens coradas com H&E, imunofluorescência multiplex (CODEX, Vectra), proteômica espacial, detecção/segmentação de núcleos, construção de gráficos de tecido ou treinamento de modelos ML em dados de patologia. Suporta 160+ formatos de lâmina incluindo Aperio SVS, NDPI, DICOM, OME-TIFF para fluxos de trabalho de patologia digital.

Provides ISO 13485 internal audit methodology for medical device quality management systems, covering audit planning, execution, nonconformity classification, and external audit preparation.

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

Flamingo: A Visual Language Model for Few-Shot Learning

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Classify medical devices under EU MDR 2017/745, build technical documentation, plan clinical evaluations, and manage post-market surveillance and EUDAMED integration.