2026-07-17T23:17:14Z https://api.figshare.com/v2/oai
oai:figshare.com:article/27992702 2024-12-09T18:17:37Z category_28879 category_26443 category_24553 portal_549 item_type_3 month_year_12_2024
10.5522/04/27992702.v1 https://figshare.com/articles/dataset/Surgical_Error_Detection_Dataset_for_SEDMamba_Enhancing_Selective_State_Space_Modelling_with_Bottleneck_Mechanism_and_Fine-to-Coarse_Temporal_Fusion_for_Efficient_Error_Detection_in_Robot-Assisted_Surgery_/27992702 https://ndownloader.figshare.com/files/51062660 https://github.com/wzjialang/SEDMamba 10.1109/LRA.2024.3505818 https://rdr.ucl.ac.uk/projects/SAR-RARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091 Xu, Jialang Jialang Xu 0000-0003-2324-7033 Sirajudeen, Nazir Nazir Sirajudeen Boal, Matthew Matthew Boal Francis, Nader Nader Francis Stoyanov, Danail Danail Stoyanov 0000-0002-0980-3227 Mazomenos, Evangelos Evangelos Mazomenos 0000-0003-0357-5996 <![CDATA[Surgical Error Detection Dataset for 'SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery']]> Computer vision Medical robotics Surgery Surgical Error Detection SAR-RARP50 Error Annotations SEDMamba 2024-12-09 2024-12-09 Dataset 2024 University College London This dataset contains error annotations used in our IEEE RA-L paper titled 'SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery'.

We consider 24 error types for 48 videos from the SAR-RARP50 dataset. Each frame is labelled as either 'normal' (0) if no error is present or 'error' (1) if any error type occurs. It contains spatial embedding sequences, binary error annotations, and corresponding frame names at the frame level, sampled at 5Hz. For detailed implementation, please refer to the SEDMamba code.

If you use this error annotation dataset, please cite the SEDMamba paper.

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