--- pretty_name: EndoDiffVQA license: other license_name: upstream-non-commercial-research license_link: LICENSE language: - en task_categories: - visual-question-answering - video-text-to-text tags: - surgery - surgical-video - endoscopy - laparoscopy - comparative-reasoning - multiple-choice - benchmark - medical size_categories: - 10K- 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. extra_gated_fields: Full name: text Institutional email: text Affiliation: text Intended use: text I will use this dataset for non-commercial academic research only: checkbox I will comply with the upstream licences of CholecT50, MultiBypass140 and CholeScore: checkbox I will not attempt to identify any patient or clinician appearing in the video: checkbox I will not redistribute the video files: checkbox extra_gated_button_content: Request access --- # 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 ```python 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).