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Medical Imaging AI & Digital Pathology

Deep learning on radiology and whole-slide pathology imaging: segmentation, classification, weakly-supervised WSI analytics, and DICOM/WSI infrastructure.

MONAIQuPathDICOMWSI
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in MONAI + QuPath + OpenSlide and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · course
Coursera · AI for Medicine Specialization
Coursera · DeepLearning.AI
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
Dataset · tool
MONAI — medical imaging deep learning
MONAI Project
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

MONAI + QuPath + OpenSlideData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on TCIA + CAMELYON / TUPAC WSI datasets

Pick one study from TCIA + CAMELYON / TUPAC WSI datasets, reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official MONAI + QuPath + OpenSlide tutorial end-to-end on real data
  • Download and explore one full dataset from TCIA + CAMELYON / TUPAC WSI datasets
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · documentation
fastai medical imaging tutorials
fast.ai
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
Dataset · tool
QuPath — digital pathology
QuPath
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

WSI tumor-region detector

Train a weakly-supervised WSI classifier for tumor regions on a public WSI dataset and report patch-level + slide-level metrics with calibration.

Deliverable: Reproducible repo + model card + clinical-style validation report

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with MONAI and Pathology AI communities (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · documentation
DICOM standard
NEMA
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
Dataset · tool
OpenSlide
OpenSlide
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · course
Python for Everybody
Coursera · Univ. of Michigan
Why: Exact sector match (Medical Imaging AI & Digital Pathology)
Dataset · dataset
TCIA — The Cancer Imaging Archive
NCI
Why: Exact sector match (Medical Imaging AI & Digital Pathology)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

Coursera · AI for Medicine Specialization
Coursera · DeepLearning.AI
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

fastai medical imaging tutorials
fast.ai
DICOM standard
NEMA
Git & GitHub fundamentals
GitHub Docs

Tools

MONAI — medical imaging deep learning
MONAI Project
QuPath — digital pathology
QuPath
OpenSlide
OpenSlide

Datasets

TCIA — The Cancer Imaging Archive
NCI

Books

R for Data Science (2e)
Hadley Wickham et al.

Communitys

Grand Challenge — medical imaging challenges
Grand Challenge

Typical career paths

Medical Imaging AI Engineer

Builds segmentation/classification models on DICOM with MONAI and validates them on clinical-grade splits.

Digital Pathology Scientist

Develops WSI pipelines in QuPath / OpenSlide with weakly-supervised deep learning.

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