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