{ "corpus": "kindly-cholect50-derived-corpus", "version": "0.1.0", "title": "Kindly CholecT50-derived clinical-episode corpus (non-commercial research)", "description": "The first at-scale, downloadable, schema-conformant episode corpus reformatting CAMMA's PUBLISHED CholecT50 annotations (the form-free CholecTriplet2022 challenge-validation label JSONs) into the open Kindly four-layer clinical-episode schema. Annotations only \u2014 NO video/imagery redistributed. Non-commercial research use only.", "schema": "kindly-episode.schema-v0.1.json", "schema_version": "0.1.1", "license": "CC BY-NC-SA 4.0", "license_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/", "license_note": "This corpus is a derivative of CAMMA's CholecT50 / CholecTriplet2022 challenge-validation annotations and is therefore released under the SAME license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). NON-COMMERCIAL research use only; ShareAlike; attribution required (cite the works listed in source.datasets).", "generated_by": "ate episode convert --from cholect50 (foodforthought-cli)", "generated_at": "2026-07-16", "source": { "datasets": [ { "name": "CholecT50", "full_name": "CholecT50 surgical action triplet dataset", "provider": "CAMMA, University of Strasbourg / IHU Strasbourg", "url": "https://github.com/CAMMA-public/cholect50", "license": "CC BY-NC-SA 4.0", "license_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/", "used_for": "L2 action triplets (, 100 triplet classes), L1 phase labels (7 phases), and L3 instrument presence (6 instruments) \u2014 all read from the per-video label JSON.", "citation": "C.I. Nwoye, T. Yu, C. Gonzalez, B. Seeliger, P. Mascagni, D. Mutter, J. Marescaux, N. Padoy. Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos. Medical Image Analysis 78:102433, 2022.", "doi": "10.1016/j.media.2022.102433", "arxiv": "2109.03223" }, { "name": "CholecTriplet2022 challenge validation set", "full_name": "CholecT50 Challenge Validation set (CholecTriplet2022)", "provider": "CAMMA, University of Strasbourg", "url": "https://s3.unistra.fr/camma_public/datasets/cholect50/CholecT50-Challenge-Validation.zip", "license": "CC BY-NC-SA 4.0", "license_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/", "used_for": "the actual, form-free, publicly-downloadable per-video label JSON files (VID68/VID70/VID73/VID74/VID75) reformatted by this corpus.", "citation": "C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, et al., D. Mutter, N. Padoy. CholecTriplet2022: Show me a tool and tell me the triplet: an endoscopic vision challenge for surgical action triplet detection. arXiv:2204.14746, 2023.", "arxiv": "2204.14746" }, { "name": "Cholec80", "full_name": "Cholec80 laparoscopic cholecystectomy dataset (EndoNet)", "provider": "CAMMA, University of Strasbourg", "url": "https://camma.unistra.fr/datasets/", "license": "CC BY-NC-SA 4.0", "license_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/", "used_for": "the underlying cholecystectomy videos and the 7-phase workflow taxonomy that CholecT50's phase labels follow.", "citation": "A.P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, N. Padoy. EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE Transactions on Medical Imaging 36(1):86-97, 2017.", "doi": "10.1109/TMI.2016.2593957" } ], "download_url": "https://s3.unistra.fr/camma_public/datasets/cholect50/CholecT50-Challenge-Validation.zip", "kindly_role": "Kindly Robotics did NOT capture, own, or license this data and has NO affiliation with CAMMA / the dataset authors. This corpus is a non-commercial RESEARCH reformatting of CAMMA's PUBLISHED CholecT50 annotations into the open Kindly episode schema, produced by running `ate episode convert --from cholect50` on the public challenge-validation labels. It is NOT a Kindly-owned, proprietary, or commercial asset and NOT proprietary clinical capture.", "content_reproduced": "Only the derived schema label structure (phase segments, action-triplet intervals, instrument-presence transitions) computed from the public per-video label JSON. NO CholecT50 video frames / imagery are downloaded, extracted, or redistributed.", "timing_provenance": "Timestamps and durations are DERIVED FROM THE REAL per-frame annotation frame indices at the dataset's native 1 fps \u2014 they are NOT representative placements. The challenge-validation release samples frames sparsely, so segment bounds reflect the actual annotated frame positions across the original video timeline.", "layers_from_real_labels": "L1 (CholecT50 phase labels), L2 (CholecT50 action triplets), L3 (CholecT50 instrument presence). L4 is intentionally EMPTY \u2014 the public corpus does not annotate workflow-context (interruptions/rework/deviations/handoffs), the layer Kindly's schema adds and never fabricates.", "phase_normalization": "Phase values are the CholecT50 published phase labels, snake_cased. The dataset's single typo 'carlot-triangle-dissection' is normalised to the canonical anatomical 'calot_triangle_dissection' (Calot's triangle); phase 6 is kept as the dataset labels it, 'gallbladder_extraction' (equivalent to Cholec80's 'gallbladder_retraction'). The exact source label string is preserved in every L1 segment's note." }, "episode_count": 5, "episodes": [ { "episode_id": "kr-cholect50-challenge-vid68", "file": "episodes/kr-cholect50-challenge-vid68.json", "source_video": "VID68", "workflow_family": "OR.laparoscopic_cholecystectomy", "duration_s": 1952.0, "fps": 1.0, "counts": { "L1": 7, "L2": 179, "L3": 67, "L4": 0 }, "phases": [ "preparation", "calot_triangle_dissection", "clipping_and_cutting", "gallbladder_dissection", "gallbladder_packaging", "cleaning_and_coagulation", "gallbladder_extraction" ], "validated": true }, { "episode_id": "kr-cholect50-challenge-vid70", "file": "episodes/kr-cholect50-challenge-vid70.json", "source_video": "VID70", "workflow_family": "OR.laparoscopic_cholecystectomy", "duration_s": 1174.0, "fps": 1.0, "counts": { "L1": 7, "L2": 96, "L3": 27, "L4": 0 }, "phases": [ "preparation", "calot_triangle_dissection", "clipping_and_cutting", "gallbladder_dissection", "cleaning_and_coagulation", "gallbladder_packaging", "gallbladder_extraction" ], "validated": true }, { "episode_id": "kr-cholect50-challenge-vid73", "file": "episodes/kr-cholect50-challenge-vid73.json", "source_video": "VID73", "workflow_family": "OR.laparoscopic_cholecystectomy", "duration_s": 1355.0, "fps": 1.0, "counts": { "L1": 7, "L2": 94, "L3": 49, "L4": 0 }, "phases": [ "preparation", "calot_triangle_dissection", "clipping_and_cutting", "gallbladder_dissection", "gallbladder_packaging", "cleaning_and_coagulation", "gallbladder_extraction" ], "validated": true }, { "episode_id": "kr-cholect50-challenge-vid74", "file": "episodes/kr-cholect50-challenge-vid74.json", "source_video": "VID74", "workflow_family": "OR.laparoscopic_cholecystectomy", "duration_s": 1633.0, "fps": 1.0, "counts": { "L1": 7, "L2": 213, "L3": 71, "L4": 0 }, "phases": [ "preparation", "calot_triangle_dissection", "clipping_and_cutting", "gallbladder_dissection", "cleaning_and_coagulation", "gallbladder_packaging", "gallbladder_extraction" ], "validated": true }, { "episode_id": "kr-cholect50-challenge-vid75", "file": "episodes/kr-cholect50-challenge-vid75.json", "source_video": "VID75", "workflow_family": "OR.laparoscopic_cholecystectomy", "duration_s": 1918.0, "fps": 1.0, "counts": { "L1": 6, "L2": 103, "L3": 41, "L4": 0 }, "phases": [ "preparation", "calot_triangle_dissection", "clipping_and_cutting", "gallbladder_dissection", "gallbladder_packaging", "gallbladder_extraction" ], "validated": true } ] }