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Imaging Evidence Path

Courses and videos

Selected, useful external instruction — not every course. We add the medical-imaging bridge; we do not duplicate their content. Certificate acquisition is not an objective.

Current

Jan Witowski — 3D Slicer Tutorial (YouTube)

The practical visual companion for Chapters 1–3. The verified playlist (11 videos, titles checked 2026-08-10) covers basics, manual/threshold/grow-from-seeds segmentation, smoothing, export, segment volume, surface vs volume rendering, 2D measurements, navigation/cropping, and DICOM anonymization. It does not include dedicated lessons on transforms/registration, segment statistics, or PyRadiomics — those are covered via the official docs and the Training Compendium.

Do not make a second generic tutorial. Use the dedicated learning/companion-3d-slicer.md — it maps each verified Witowski lesson to a concept (Ch. 1–3) + a small RIDER exercise, and links the official 3D Slicer Training Compendium (training.slicer.org) for the topics the playlist does not cover.

Playlist: https://www.youtube.com/playlist?list=PLeaIM0zUlEqswa6Pskg9uMq15LiWWYP39

StanfordOnline — Statistical Learning with Python (SOHS-YSTATSLEARNINGP)

Based on An Introduction to Statistical Learning, with Applications in Python (ISLP; James, Witten, Hastie, Tibshirani, Taylor, Springer 2023).

  • This course owns the general ML statistics.
  • learning/04-statistical-learning-and-validation/00-overview.md is the medical-imaging companion map: which chapters to attend to closely, which to skim, and the imaging-specific consequences (leakage, calibration, domain shift, survival). Use the translation table there to read each ISLP chapter against a TRACE-CT example.

Stanford Medicine BMR — MRI Physics Education Materials

The curated video backbone for Chapter 8 (MRI). University source (Body MRI Research Group, Stanford Radiology), free for education: short, intuitive overviews originally for radiology residents, organised by topic.

  • Where: https://med.stanford.edu/bmrgroup/education/mri-physics.html (VERIFIED)
  • Use it for the visually difficult MRI ideas: relaxation/T1–T2 contrast, k-space and resolution/field-of-view, fat suppression/inversion recovery, diffusion, and the spin-echo/gradient-echo sequence overview.
  • How the chapter uses it: the MRI lessons point to the relevant section on that page (e.g. “MRI Contrast Mechanisms → Relaxation, T1 and T2 Contrast”); watch the section rather than chasing a specific timestamp.
  • What it is not: a replacement for a full MRI-physics text when you need depth (see the chapter’s further reading: Hashemi/Bradley, Bushberg).

High-value further resources (verify before adding)

Evaluate when interest is active. Do not add speculatively.

  • Advanced medical-imaging / radiomics workshops — selected RSNA / SIIM / MICCAI educational material (verify current availability and licence before linking).
  • Advanced imaging-AI courses (e.g. AI4Imaging-style) — confirm the course is still offered and current before linking.
  • IBSI / PyRadiomics tutorials — these are tooling, covered in standards-and-reference.md; promote here only if a structured course form adds value.

Rule

One activity that teaches a concept and solves a real TRACE-CT problem beats three passive courses. Prefer exercises in the learning chapters over collecting course completions.