Skill UI

Pydicom is a Python toolkit for reading, writing, and manipulating DICOM medical imaging files, helping teams extract pixel data, manage metadata, anonymize records, convert to standard formats, and integrate with PACS workflows in healthcare research and radiology applications.

The BIDS skill provides a comprehensive framework for organizing, standardizing, and managing complex neuroscience and biomedical datasets. It covers querying, validating, and converting various modalities (MRI, EEG, MEG, etc.) to ensure compliance with the BIDS standard for research reproducibility and efficient data sharing.

This skill provides a comprehensive guide for authors submitting papers in biomedical imaging algorithms, microscopy, and medical imaging to top-tier conferences like ISBI. It helps authors strategically frame their work, assess scope fit, ensure strong evidence, and understand the unique requirements of the venue, ensuring the manuscript is optimized for conference review standards.

A comprehensive guide for authors submitting manuscripts to IEEE Transactions on Medical Imaging (TMI). It details the journal's specific scope, required technical rigor, necessary validation standards, and structure, emphasizing that contributions must be medical-imaging-specific rather than generic computer vision methods. This tool helps authors assess fit, re-frame their work, and anticipate submission requirements.

This guide provides expert advice on positioning, framing, and validating medical imaging AI manuscripts for the MICCAI conference. It helps authors tailor their research to meet the venue's specific requirements, focusing on clinical-task grounding, robust validation, and alignment with core scopes like computational anatomy and image-guided intervention. Essential for optimizing conference submission strategy.

A comprehensive guide detailing the stringent methodological and reporting standards required for submitting medical imaging research (diagnostic, AI, interventional) to a top-tier radiology journal. It covers expectations for diagnostic accuracy (STARD), AI model validation (CLAIM), sample size justification, reproducibility, and ensuring clinical relevance, helping authors frame their work for optimal journal fit.