Session Kit: Module 14: Computer Vision for EM
Everything needed to run Module 14 as a taught session: prep, timing, materials, misconceptions, rubric.
Generated from modules/module14.md. Edit the module page, not this file.
At a glance
| Duration | 4-5 hours |
| Capability target | Design and evaluate a CV pipeline for EM imagery that is fit for a specific connectomics task and explicitly bounded by known failure modes. Concretely: choose an architecture from the shape of the task rather than from the benchmark leaderboard, decompose error into merges and splits instead of reporting one score, convert that decomposition into a downstream cost using a ratio your team has actually measured, and write a release gate that says in advance what result would stop the model from shipping. |
| Learners leave with | metric table with biological interpretation, split by region |
Before you walk in
- You can state the capability target in one sentence without reading it.
- You have one worked example you will narrate, including where you are unsure.
- Data access works — accounts, viewer, notebook — verified today, not last week.
- The rubric is visible to learners before they start, not after.
- You have decided what “uncertain” earns, and you will say so out loud.
Learners should arrive having covered:
- Image-processing and matrix basics
- Foundational ML familiarity
Pre-class preparation set for learners:
- Read metrics and QA for VI and ERL, and artifact taxonomy for what the model must survive.
- Bring or download one EM subvolume with visible artifacts.
- Be ready to state which downstream product your segmentation would feed.
Materials
Run of show
| Time | Segment | Your note |
|---|---|---|
| 00:00-08:00 task framing + exemplar failure modes. Show one split and one merge in the viewer and ask which is worse; collect reasons before giving the answer. | ||
| 08:00-20:00 choose metrics tied to downstream biology. Each learner writes the metric they would gate on and the threshold, before seeing any model output. | ||
| 20:00-34:00 evaluate baseline vs candidate model. Learners compute or are given VI components, ERL, and error counts for two models, then solve for the break-even merge-to-split ratio. | ||
| 34:00-46:00 error taxonomy and triage discussion. Sample failure cases, classify each by cause, and identify which causes augmentation could have addressed. | ||
| 46:00-56:00 model card drafting, including at least one unsupported use and the region breakdown of the metrics. | ||
| 56:00-60:00 competency check | each learner states their release gate as a sentence that could fail. |
The activity
Scenario: Compare two segmentation-support CV models for an EM subvolume. You are given the model outputs, a proofread ground-truth subvolume of roughly 40 mm of traced path drawn from a clean region, and a second, smaller ground-truth patch from a region containing a partial fold and two lost sections. Your team maintains the production segmentation and must recommend one model.
- Compute or tabulate VI with its split and merge components, ERL, and error counts for both models, reported separately for the clean and the artifact-heavy region.
- Solve for the merge-to-split cost ratio at which the two models tie, and state which side of that ratio your team is on and how you know.
- Sample at least 15 failure cases across both models and classify each by cause.
- Write a release gate: a numeric criterion, decided before looking at the winner, that the chosen model must pass.
- Draft the model card limitation statement, including one use you would refuse to support.
What learners hand in
- metric table with biological interpretation, split by region
- failure-case log with causes tallied
- break-even ratio calculation with the assumption behind it named
- model-card limitation statement
Misconceptions to target
These are the errors this session exists to prevent. Surface them in the debrief rather than pre-empting them in the lecture — a misconception a learner has voiced is far easier to correct than one they are holding silently.
- They may believe: One architecture solves all EM tasks equally well.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: A higher benchmark score means safer downstream use.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Visual plausibility is sufficient validation.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: The held-out ground truth is representative of the volume.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: More augmentation is always better than less.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
Naming the norm
Every session is a chance to make one piece of the hidden curriculum explicit. Pick a moment where you would normally just do the professional thing, and say out loud why you are doing it — then ask whether anyone was taught that.
For this session, the candidate is whichever norm the activity most depends on: stating an assumption in the same sentence as the claim, recording the version a number came from, or saying “uncertain” and having it count as a real answer. See the hidden curriculum for the collected set and why naming them is a fairness intervention rather than etiquette.
Assessment
- Minimum pass: clear task-model rationale, biologically relevant metrics reported with merge and split separated, explicit limitations naming at least one unsupported use.
- Strong performance: robust failure analysis by cause, a release gate written before the result was known, and a downstream cost argument that names the merge-to-split ratio as a measured quantity rather than an assumption.
- Failure modes: metric-only reasoning, pooling clean and artifact-heavy regions into one number, weak split design, no deployment boundaries, thresholds chosen after seeing which model they would favor.
Grade the reasoning, not the answer. A correct call with no evidence chain should not outscore a well-reasoned incorrect one — and saying so publicly changes behaviour within one session.
Exit prompt
Document one CV result with one supported use case and one forbidden use case.
If this session goes wrong
- Nobody talks in the debrief. You asked “any questions?” Ask instead: “Which cue would you drop first if the data got worse?”
- Everyone finishes early. They are pattern-matching, not judging. Give an ambiguous case where the answer is “uncertain” and see what happens.
- Nobody finishes. The scaffolding came off too fast. Work the next case together rather than pressing on.
- A learner is silently lost. The most likely cause is unstated vocabulary. Point them at the dictionary and check back.