medical-imaging-guide
Medical image analysis with deep learning for research applications
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Medical Imaging Guide A skill for applying deep learning to medical image analysis in research settings. Covers common imaging modalities, preprocessing pipelines, architecture selection for classification and segmentation tasks, handling small datasets with transfer learning and data augmentation, evaluation metrics specific to medical imaging, and regulatory and ethical considerations for clinical translation. ## Imaging Modalities and Data Characteristics ### Common Modalities in Research ``` Modality Overview: X-ray / Radiography: - 2D grayscale images - Resolution: typically 2000x2000 to 4000x4000 pixels - Format: DICOM (.dcm) - Common tasks: pneumonia detection, fracture detection, cardiomegaly screening - Dataset examples: CheXpert, MIMIC-CXR, NIH ChestX-ray14 CT (Computed Tomography): - 3D volumetric data (stack of 2D slices) - Resolution: 512x512 per slice, 50-500+ slices - Format: DICOM series, NIfTI (.nii.gz) - Common tasks: lung nodule detection, organ segmentation, COVID-19 screening - Dataset examples: LUNA16, DeepLesion, TotalSegmentator MRI (Magnetic Resonance Imaging): - 3D volumetric, multiple sequences (T1, T2, FLAIR, DWI) - Resolution: 256x256 to 512x512 per s
- Imaging Modalities and Data Characteristics
- Common Modalities in Research
- Preprocessing Pipeline
- Standard Preprocessing Steps
- Model Architecture Selection
- Task-Specific Architectures
- Handling Small Datasets
- Data Augmentation for Medical Images
- Transfer Learning Strategies
- Evaluation Metrics
- Medical Imaging Specific Metrics
- Ethical and Regulatory Considerations
What does the medical-imaging-guide skill do?
Medical image analysis with deep learning for research applications
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill medical-imaging-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.