MedicalLab Wiki

This is the official knowledge base of for the MedicalLab, the Canada Research Chair (CRC) in Medical Imaging and Assisted Interventions, headed by Professor Samuel Kadoury. It includes useful information about subjects ranging from medical image analysis, to deep learning, experimental design, and server maintenance.

Sitemap:

• MedicalLab Wiki
  1. Setup
  2. Workflow / Beginner
  3. Workflow / Intermediate
  4. Workflow / Advanced
  5. Exiting
  6. References
     1. Contacts
     2. Datasets
     3. FAQ
     4. Glossary
     5. Publications
     6. Tooling
     7. Tutorials

1. Setup

➡ Start here if you're a new member of the lab.

You, a new intern/master/PhD/postdoc student. You've joined MedicalLab. You're wondering to yourself: now what? Where do I go? What accesses do I need? Who do I contact?

1. Administrative steps: getting accesses, finding offices, finding contacts.
2. Research steps: getting familiar with current literature and technologies.
3. Technical steps: setting up a work environment and initial walkthrough of a typical codebase.

Workflow

Here you will find in detail explanation and documentation pertaining to the various discipline to link together within our lab: from machine learning to experimental design to statistical analysis to to medical image analysis to systems administration, etc.

2. Beginner

➡ Read this if you just want to start running deep learning models, fast.

Learn about machine learning models, data loading, metrics, and training and validation.

3. Intermediate

➡ Read this if you want to know more about deep learning for medical imaging.

These pages contain documentation on advance training and evaluation techniques, description of medical imaging specific processes and links to other works of interests to our research projects. Long term students, e.g. Master and PhD students, should consult this regularly.

4. Advanced

➡ Read this if you're a resources administrator for the lab.

This page describe things related to the administration of the lab such as managing resources, paperwork and personnel. Here are also advanced tip and tricks to optimize your experiments when running on Linux compute servers.

1. Resources: lab seatings, workstations, Polymtl/CRCHUM HR, past and current contact students information, meeting rooms.
2. Paperwork: contracts to access CRCHUM.
3. Manual Segmentation: how to segment an image by hand using ITK-SNAP.
4. Server Maintenance: Commands to upgrade, fix storage issues, etc.
5. Data Acquisition: Extracting medical data from the hospital servers.

5. Exiting and Graduating

➡ Congrats, you've graduated!

Everyone who is exiting the lab should go through these steps to ensure a proper hand-off of their code and datasets.

6. References

1. Datasets: both public and in-house, as well as where they are stored on our servers.

2. Frequently Asked Questions (FAQ):

   1. What is deep learning?
   2. What is medical image analysis?
   3. What are medical images?
   4. What are medical interventions?
   5. Can I work remotely on the lab servers?
   6. How can I get access to the CRCHUM?
   7. Who do I contact about money-related questions?
   8. Do we have a poster printer and carrier for conferences?

3. Glossary: domain specific terminology, acronyms and abbreviations.

4. Publications: a list of journals/conferences we've published at or where to find competition inspiration.
5. Tooling: popular and useful libraries.

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