EndoDiffVQA contains surgical video derived from CholecT50, MultiBypass140 and CholeScore. Access is granted for non-commercial academic research only, and is subject to the licence of each upstream release (CholecT50 and MultiBypass140 are CC BY-NC-SA 4.0). You remain responsible for complying with those licences and for obtaining any further permission your use requires.

EndoDiffVQA

Comparative surgical video question answering. Every item shows two surgical video clips and asks one four-option multiple-choice question that cannot be answered from either clip alone — the answer is a relation between the videos (same / different / present in only one / present in neither).

10,939 items · 7,289 train / 1,350 validation / 2,300 test · 1,762 clips (33.2 GB) · 4 sources · 4 categories · 20 attributes · 14 question templates

This release is a pin: a frozen snapshot of the template-MCQ dataset as built on 2026-09-27, from commit 8cdde6465a6a. Digests of every input file are in metadata/pin.json.

Sources

source	procedure	items	train / val / test	categories	clips	video
`cholect50_caption`	laparoscopic cholecystectomy	6,190	4,396 / 632 / 1,162	Action, Anatomy, Tool	1,224	1.44 GB
`multibypass_caption`	laparoscopic Roux-en-Y gastric bypass	2,060	1,104 / 468 / 488	Action, Anatomy, Tool	218	1.15 GB
`chole_score`	laparoscopic cholecystectomy	1,684	1,089 / 175 / 420	Skill	201	22.82 GB
`multibypass_skill`	laparoscopic Roux-en-Y gastric bypass	1,005	700 / 75 / 230	Skill	119	7.78 GB

cholect50_caption and multibypass_caption carry the caption-derived categories (Anatomy, Tool, Action); chole_score and multibypass_skill carry Skill. No item ever pairs clips from two different sources, and no item pairs a CholeScore video with a MultiBypass one.

Categories and attributes

category	items	attributes
Anatomy	3,326	4	`pathology`, `presence`, `color`, `position`
Tool	3,241	4	`count`, `presence`, `engagement`, `entry side`
Skill	2,689	9	`cystic artery dissection`, `fossa dissection`, `cystic duct dissection`, `Calot exposure`, `bimanual_dexterity`, `efficiency` …
Action	1,683	3	`rationale`, `interaction`, `target`

Full taxonomy with per-split counts: metadata/taxonomy.json.

Question construction

Questions are template-generated, not model-written: a template renders a stem plus four options over facts read off the source annotation, so the ground truth is traceable to the annotation rather than to a language model. metadata/templates.json lists every template with its stem and the relations it realises.

Two option families:

Family E — enumerated lattice — the four options are drawn from a fixed cell set, identical whatever the truth, so the shown set leaks nothing about the answer
Family V — value-bearing — options quote concrete observed values (a colour, a count, a rubric level); distractor values come from the same source's value pool

Each distractor carries a code in distractor_codes (aligned with options):

correct — the true option
D1 — SWAP — the two videos' values or the direction of the gap are exchanged
D2 — COLLAPSE — the wrong same/different verdict
D3 — MAGNITUDE — right verdict, wrong size of the gap
D4 — SUBSTITUTE — a plausible value drawn from the source's pool for this subject

Balance. Answer letters are a: 25.0% / b: 25.0% / c: 25.0% / d: 25.0%, so the majority-letter baseline is 25.1%. The yes/no verdict splits No 56.4% / Yes 43.6%; a text-only model that always answers the majority verdict gets 56.4%. The skew is uneven by family — E: No 50.7% / Yes 49.3%, V: No 63.7% / Yes 36.3% — so report Family E and Family V separately. Family E items show a fixed option set independent of the truth, so the option text does not reveal the answer.

Relations

relation is the coarse answer type, cell the fine option slot it was drawn from (metadata/templates.json → _legend):

SAME (4,715) — both videos carry the same value, or the same standard
DIFFERENT (3,774) — the two videos differ
ONLY (2,054) — the subject is present in only one of the two videos
NEITHER (396) — the subject is present in neither video

Splits

Splits are cut to be disjoint in the unit that carries the label — case for the Skill sources, clip for the caption sources — so no video appearing in train reappears in val or test. The realised overlap counts are in metadata/stats.json under disjointness.

Fields

field	type	meaning
`id`	str	`{source}-template_mc-n2-{split}{pair}-{index}`
`split`, `source`, `qa_type`, `n_compare`	str, str, str, int	`split` is `train`/`val`/`test` (the `datasets` split is named `validation`); `qa_type` is `template_mc`; `n_compare` is 2 throughout
`category`, `attribute`	str	Anatomy / Tool / Action / Skill, and the compared attribute
`question`	str	stem with the four options inlined — feed this verbatim
`question_stem`	str	the stem alone
`options`	list[str]	the four option texts, in order a, b, c, d
`option_a` … `option_d`	str	the same, as flat columns
`answer_letter`, `answer`, `answer_text`	str	`c`; `(c) Yes — …`; `Yes — …`
`verdict`	str	`Yes` / `No` — the polarity of the correct option
`verdict_shared`	bool	whether the verdict is the same for every item of this group
`videos`	list[str]	release-relative clip paths, in order
`video1`, `video2`	str	the same, as flat columns
`template_id`, `family`, `relation`, `cell`	str	how the item was built
`distractor_codes`	list[str]	per-option code, aligned with `options`
`subject_keys`	list[str]	what the question is about (entity, tool, criterion, or [step, domain])
`pair_id`, `qa_index`	str	position within the source build
`phase`	str \| null	surgical phase of the clip pair (caption sources)
`value_a`, `value_b`	str \| null	the compared values (Family V, caption sources)
`clip1`, `clip2`	int \| null	source clip ids (caption sources)
`distractor_pool`	str \| null	JSON: value → where the distractor was drawn from
`criterion`, `step`	str \| null	rated criterion and surgical step (Skill sources)
`score_a`, `score_b`, `delta`	int \| null	the two ratings and their difference (Skill sources)
`levels_shown`	list[int]	rubric levels quoted by the options (S2 only; else empty)
`unit1`, `unit2`, `case1`, `case2`	str \| null	rated unit and case id (Skill sources)

Usage

from datasets import load_dataset
from huggingface_hub import snapshot_download

ds = load_dataset("ethanshili/EndoDiffVQA", split="test")          # metadata only, ~MBs
root = snapshot_download("ethanshili/EndoDiffVQA", repo_type="dataset")  # + video, 33 GB

item = ds[0]
print(item["question"])
paths = [f"{root}/{p}" for p in item["videos"]]           # the two clips, in order

Grading is exact-match on answer_letter. Report accuracy broken down by source, category, attribute and relation — aggregate accuracy hides that the four sources ask different questions.

Known limitations

Resolution is a recording-setup fingerprint. Clip shapes are 854x480 (×1,442), 1920x1080 (×205), 600x480 (×115). 11.3% of items pair two clips of different size, and the share is uneven across splits (train 10.2%, val 0.0%, test 21.2%), so a model keying on frame geometry is not equally visible in validation and test. Normalisation to a common 784×448 bound is planned and not applied here.
Option text of the CholeScore rubric levels (S2) is a DRAFT layer. Levels 1/3/5 quote the upstream anchor verbatim; levels 2 and 4 are authored interpolations, and S2 is restricted to 1/3/5 in this build.
Templates are not equally supplied. Per-template counts range widely (see metadata/stats.json → split_x_template); C2 in particular is far below the others.
n_compare is 2 for every item. The 3- and 4-video comparisons of the open-ended EndoDiffVQA splits are not part of this MCQ release.

Licence and citation

Non-commercial academic research only. Each source keeps its upstream licence:

cholect50_caption — CholecT50 (CAMMA, University of Strasbourg): CC BY-NC-SA 4.0 (registration form) — per the upstream project page ⚠️ not verified against an upstream LICENSE file — confirm before relying on it
multibypass_caption — MultiBypass140 (CAMMA — StrasBypass70 + BernBypass70): CC BY-NC-SA 4.0 (non-commercial research, no registration)
chole_score — CholeScore (OSATS STS skill annotations over cholecystectomy video): unknown — confirm with the data provider before redistributing ⚠️ not verified against an upstream LICENSE file — confirm before relying on it
multibypass_skill — MultiBypass140 skill annotations (GOALS): CC BY-NC-SA 4.0 (non-commercial research, no registration)

Cite the upstream datasets alongside this release:

Nwoye et al., Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos, Medical Image Analysis, 2022.
Lavanchy et al., Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery, IJCARS, 2024.
Lavanchy et al., IJCARS, 2024 (skill annotations of the same release).

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Total file size:

33.2 GB