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REVIEW 4 major objections 5 minor 36 references

SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read SWAG recasts surgical phase anticipation as generative sequence prediction and shows it beats simple priors on variable procedures.

desk verdict The paper's real contribution is dense long-horizon surgical phase anticipation, but the headline advantage over a simple baseline on AutoLaparo21 is not yet established—seven test videos, no error bars. read the letter →

arxiv 2412.18849 v4 pith:YK5OGYSL submitted 2024-12-25 cs.CV cs.LG

Maxence Boels , Yang Liu , Prokar Dasgupta , Alejandro Granados , Sebastien Ourselin This is my paper · ORCID
classification cs.CVcs.LG
keywords surgicalworkflowanticipationphaserecognitiongenerativesequencemodelingremainingtimeregressionclasstransitionprobabilitiessingle-passdecodingCholec80AutoLaparo21
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that long-term surgical phase anticipation is best framed as generative sequence prediction, not as single-event regression. It proposes SWAG, a transformer-based framework that, from the observed video up to the current minute, generates a minute-by-minute sequence of future surgical phases over horizons up to 60 minutes. The central comparison is against two simple baselines: extending the current phase unchanged (Naive1) and sampling from training-set class transition probabilities conditioned on the recognized phase (Naive2). The claim is that the generative models match or beat these priors on frame-level F1 and, importantly, keep segment-level temporal coherence (SegF1), which the baselines lose. If true, this gives operating-room systems a way to visualize the remaining workflow and estimate completion time, not just the next event.

What carries the argument

The load-bearing mechanism is the class transition probability embedding inside the single-pass decoder. For each future minute index $h_n$, the token input is initialized with the training-set probability $P(y_{t+h_n \cdot 60}=j \mid y_t=i)$, the chance that class $j$ is active $h_n$ minutes after current class $i$; after a linear projection this vector is added to a learned token embedding and sinusoidal positional encoding. The decoder then uses cross-attention between these future tokens and compressed past context tokens from a windowed self-attention encoder with cumulative max-pooling, generating all $N$ future phases in one forward pass. A second mechanism, regression-to-classification (R2C), sorts the predicted remaining times to each phase into ascending order and bins them into a discrete high-resolution phase sequence, letting a regression-trained model produce the same kind of anticipation output.

What would settle it

Collect a second set of expert annotations of plausible future phase sequences for a sample of Cholec80 and AutoLaparo21 test moments, and re-score SWAG-SP* with SegF1 where a prediction is credited if it matches any expert-plausible trajectory. If scores rise substantially relative to single-ground-truth scoring, the single-future assumption, not the model, is the main cap on reported performance; if they do not rise, the model's errors are not an artifact of the metric.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that a single-pass transformer decoder, whose future tokens are initialized with class transition probabilities extracted from training data (SP*), produces coherent minute-level forecasts of future surgical phases and outperforms both naive baselines on the more variable AutoLaparo21 dataset, with mean F1 of 41.3% over 30 minutes and SegF1 of 34.8%. On Cholec80 the same model reaches 32.1% mean F1 over 20 minutes, while Naive2 reaches 39.5% frame-level F1 but only 11.9% SegF1, which the paper reads as evidence that simple priors exploit structured workflows without temporal coherence. The same single-pass decoder, trained for remaining-time regression, achieves weighted MAEs of 0.32 and 0.48 minutes at 2- and 3-minute horizons, competitive with Bayesian and IIA-Net baselines, and the proposed regression-to-classification (R2C) mapping turns regression outputs into discrete phase sequences. The paper concludes that generative decoding unifies recognition and anticipation and becomes increasingly valuable as surgical workflow variability increases.

Load-bearing premise

The framework assumes that for each observed moment there is one correct future phase sequence, so a prediction is graded only against the single annotated future; if several futures are equally plausible, the loss and the F1/SegF1 metrics penalize valid predictions and the reported scores understate the model's true anticipative skill.

Editorial extensions

If this is right

  • A single model trained with maximal horizon $N$ can be evaluated at any shorter horizon without retraining, so the same checkpoint serves both short- and long-term anticipation.
  • Dense minute-level future phase sequences give a direct visual completion of the remaining workflow, which the paper argues supports intraoperative awareness and guidance beyond single next-event prediction.
  • On structured workflows like cholecystectomy, simple priors remain strong, so generative modeling's practical edge appears when phase order and durations are more variable, as in AutoLaparo21.
  • Because regression outputs can be converted into classification sequences via R2C, remaining-time estimation and phase anticipation cease to be separate tasks in this framework.
  • Segment-level F1 (SegF1) rewards correct phase boundaries and penalizes oversegmentation, so the reported advantage of generative models over naive baselines is not just frame-counting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If multiple futures are equally plausible, the reported F1 numbers are likely conservative: re-scoring predictions against the best-matching expert-annotated future trajectory would raise both frame-level and SegF1 scores; this is an extension of the limitation the paper itself acknowledges.
  • The class transition probability priors are computed once from the training set; a testable extension is to update them online per procedure or per surgeon, which could sharpen the SP* embedding as more of the current surgery is observed.
  • The same decoder could generate instrument-usage or action tokens alongside phase tokens, since the token-level conditioning is not phase-specific; that would bring the framework closer to full workflow generation.
  • The strong Naive2 result on Cholec80 suggests that future surgical anticipation benchmarks should report a prior-conditioned baseline like Naive2 as a mandatory comparison point, otherwise model gains on structured datasets can be overstated.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces SWAG, a transformer-based framework for simultaneous surgical phase recognition and long-term phase anticipation. It proposes two generative decoding schemes (single-pass and auto-regressive), a prior-knowledge embedding based on class transition probabilities, and a regression-to-classification mapping. Evaluated on Cholec80 and AutoLaparo21, the single-pass model with transition-probability embeddings obtains mean F1 scores of 32.1% over 20 minutes and 41.3% over 30 minutes, respectively, and competitive remaining-time regression results.

Significance. If the reported results are reliable, the paper would advance surgical workflow anticipation by moving from single-event prediction to dense, long-horizon sequence generation, and by unifying recognition and anticipation in one model. The idea of conditioning future tokens on empirically estimated class transition probabilities is simple and transferable. Strengths include evaluation on two public datasets, comparison with naive baselines and prior regression methods, and clear documentation of the SegF1 metric. However, the paper does not provide code, error bars, or statistical tests, and the most important comparative claim rests on a very small test set.

major comments (4)
  1. [4.1, Table 1] The central claim that SP* outperforms the conditional-probability baseline Naive2 on AutoLaparo21 (41.3 vs. 34.3 F1) rests on a test set of only 7 videos, with no confidence intervals, bootstrap estimates, or per-video results reported. On Cholec80 the same SP* model is 7.4 points below Naive2, so the 'versatility' conclusion depends entirely on this one unquantified gap; a single atypical procedure could shift the mean by several points. Please report per-video scores and interval estimates (e.g., bootstrap CIs or a paired test) for the headline F1 comparisons.
  2. [4.2, Table 3] The RSD comparison is not controlled: the table reports BD-Net's results on random 4-fold splits while SWAG-SP is evaluated on consecutive 60/20 splits, as the note under the table admits. The text says 'using 4-fold cross-validation,' but the implementation differs from the comparison method's protocol. This makes the 'ranks second on MAE-5 and MAE-ALL' claim unsupported; please re-run on exactly the same split scheme or present the comparison as a qualitative reference only.
  3. [8.2, Segment-based F1 (SegF1)] The SegF1 metric relies on several hand-set parameters—IoU threshold 0.25, EOS weight 0.5, and EOS capping at 4/8 minutes—without any sensitivity analysis or demonstration that the conclusions are robust to their choice. Since the reported advantages over baselines are modest (e.g., Table 1, AutoLaparo21: SP* 41.3 vs. SP 38.4 vs. Naive2 34.3), it is important to show that the ranking is not an artifact of these thresholds.
  4. [5, Limitations paragraph] The paper correctly acknowledges the 'assumption of single valid future trajectories,' but this assumption also limits the evaluation metrics: cross-entropy and SegF1 compare against one annotated future sequence. If multiple futures are plausible, the reported F1 numbers systematically penalize valid predictions and the method's true predictive skill is understated. Please quantify the extent of this issue (e.g., annotator agreement on plausible futures, or a comparison with a distribution-aware metric) so the reader can judge the practical significance of the reported numbers.
minor comments (5)
  1. [3.2] The symbol p is used for both the key-pooling representation (p = max{...}) and the future-token probability vector p_t (Eq. 5); please disambiguate the notation.
  2. [3.1, Eqs. (1)-(2)] The notation h0 = 0 and then hn ∈ {1,2,...,N} is slightly confusing because h0 is not part of the future index set; consider writing {0, h1, ..., hN} explicitly.
  3. [Table 3] The standard deviations are reported without the number of folds or test sizes used to compute them; please specify these details.
  4. [Throughout] The method name is typeset inconsistently: 'SW AG' appears with a space in the abstract and Section 1, while 'SWAG' appears in the text (e.g., 'SWAG-SP'); please unify the spelling.
  5. [8.1.2, Eq. (9)] The IoU formula would be easier to follow if the union were written explicitly as the sum of lengths minus the intersection; the current compressed expression is less readable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the anticipation results are held-out empirical evaluations, with the transition-probability embedding acting as an input feature rather than a fitted target.

full rationale

The paper's central claims are empirical: SP* is evaluated on held-out test splits of Cholec80 and AutoLaparo21, and the reported F1/MAE numbers are computed against ground-truth future phases. The class transition probability tensor P is derived from the training set and used as an input embedding (Eqs. 3-7), not as the prediction target. The model is trained with cross-entropy against actual future labels, and the ablation SP* vs. SP isolates the contribution of this embedding. The Naive2 baseline uses the same P directly, so the comparison is meaningful rather than circular. R2C is a deterministic post-processing of regression outputs into discrete phase sequences, not a prediction defined in terms of the input. Self-citations to LoViT and SKiT supply the recognition backbone and are published prior work with external benchmarks; they are not invoked to justify the anticipation results. No equation in the paper defines the output as equivalent to the input, and no fitted parameter is renamed as a prediction. Thus the derivation chain is self-contained with respect to the reported evaluations.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claims rest on empirical priors (transition matrix), hand-set evaluation parameters, and standard supervised assumptions. No new physical or ontological entities are introduced.

free parameters (6)
  • Class transition probability matrix P = Estimated from training set labels
    Empirical P(i to j at horizon hn) is used both as the Naive2 baseline and as input embeddings in SP*. It encodes strong prior structure and is not independently derived.
  • Anticipation interval = 60 seconds
    Chosen based on ablation in SM Fig. 10 as the sampling step for future phase tokens; all reported F1 numbers depend on this granularity.
  • Context length L = 1440 frames (24 minutes)
    Selected by ablation in SM Fig. 9; recognition and anticipation are conditioned on this window, so the reported scores are tied to this choice.
  • EOS capping = 4 min (Cholec80), 8 min (AutoLaparo21)
    Hand-set caps on the number of EOS samples per sequence used in F1 and SegF1; they limit EOS dominance but are post hoc evaluation choices.
  • SegF1 IoU threshold and EOS weight = tau=0.25, weight=0.5
    Chosen by the authors for the segment metric; different thresholds would change the reported SegF1 values.
  • Anticipation horizon N = 20 min (Cholec80), 30 min (AutoLaparo21)
    Maximum prediction horizon used for evaluation; results are reported over these horizons, and performance degrades beyond 15-20 minutes.
assumptions (6)
  • domain assumption Train and test videos are drawn from the same workflow distribution.
    The empirical transition matrix P and the learned model are evaluated on held-out splits of the same datasets; transfer to other surgical centers or procedure types is not established.
  • domain assumption A single valid future trajectory exists for each current time point.
    Acknowledged in Limitations; the objective functions and metrics penalize any deviation from the single annotated future.
  • domain assumption The predicted current class is a reliable conditioning signal for the transition prior.
    SP* and Naive2 condition on the recognized current phase; errors in recognition propagate to the prior and to the generated future.
  • domain assumption The 1-minute discretization and 1 fps sampling adequately represent surgical phase transitions.
    Supported by ablations on these datasets, but short phases or transitions within a minute are lost.
  • domain assumption Vision transformer features contain sufficient information to forecast 20-30 minutes ahead.
    The model relies on LoViT frame embeddings; no experiment isolates whether the bottleneck is the features or the decoder.
  • standard math Transformer attention and gradient-based optimization function as described in prior work.
    The decoder relies on standard transformer machinery; no new mathematical framework is introduced.

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Cite this review

Pith. "Pith review of SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation." pith.science (2026). https://pith.science/paper/YK5OGYSL

@misc{pith2026241218849,
  author       = {Pith},
  title        = {Pith review of: SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YK5OGYSL}},
  note         = {Machine review of arXiv:2412.18849}
}
read the original abstract

While existing approaches excel at recognising current surgical phases, they provide limited foresight and intraoperative guidance into future procedural steps. Similarly, current anticipation methods are constrained to predicting short-term and single events, neglecting the dense, repetitive, and long sequential nature of surgical workflows. To address these needs and limitations, we propose SWAG (Surgical Workflow Anticipative Generation), a framework that combines phase recognition and anticipation using a generative approach. This paper investigates two distinct decoding methods - single-pass (SP) and auto-regressive (AR) - to generate sequences of future surgical phases at minute intervals over long horizons. We propose a novel embedding approach using class transition probabilities to enhance the accuracy of phase anticipation. Additionally, we propose a generative framework using remaining time regression to classification (R2C). SWAG was evaluated on two publicly available datasets, Cholec80 and AutoLaparo21. Our single-pass model with class transition probability embeddings (SP*) achieves 32.1% and 41.3% F1 scores over 20 and 30 minutes on Cholec80 and AutoLaparo21, respectively. Moreover, our approach competes with existing methods on phase remaining time regression, achieving weighted mean absolute errors of 0.32 and 0.48 minutes for 2- and 3-minute horizons. SWAG demonstrates versatility across generative decoding frame works and classification and regression tasks to create temporal continuity between surgical workflow recognition and anticipation. Our method provides steps towards intraoperative surgical workflow generation for anticipation. Project: https://maxboels.com/research/swag.

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Reference graph

Works this paper leans on

36 extracted references · 33 canonical work pages

  1. [1]

    BMJ Quality & Safety (2018)

    Sexton, K., Johnson, A., Gotsch, A., Hussein, A.A., Cavuoto, L., Guru, K.A.: Anticipation, teamwork and cognitive load: chasing efficiency during robot- assisted surgery. BMJ Quality & Safety (2018)

    work page 2018

  2. [2]

    Simulation in Healthcare (2010)

    Yurko, Y.Y., Scerbo, M.W., Prabhu, A.S., Acker, C.E., Stefanidis, D.: Higher mental workload is associated with poorer laparoscopic performance as measured by the nasa-tlx tool. Simulation in Healthcare (2010)

    work page 2010

  3. [3]

    International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)

    Czempiel, T., Paschali, M., Keicher, M., Simson, W., Feußner, H., Kim, S.T., Navab, N.: Tecno: Surgical phase recognition with multi-stage temporal convo- lutional networks. International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)

    work page 2020

  4. [4]

    Medical Image Analysis (2023)

    Liu, Y., Boels, M., Garc ´ ıa-Peraza-Herrera, L.C., Vercauteren, T.K.M., Dasgupta, P., Granados, A., Ourselin, S.: Lovit: Long video transformer for surgical phase recognition. Medical Image Analysis (2023)

    work page 2023

  5. [5]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)

    Liu, Y., Huo, J., Peng, J., Sparks, R., Dasgupta, P., Granados, A., Ourselin, S.: Skit: a fast key information video transformer for online surgical phase recogni- tion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2023)

    work page 2023

  6. [6]

    In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, pp

    Aksamentov, I., Twinanda, A.P., Mutter, D., Marescaux, J., Padoy, N.: Deep neural networks predict remaining surgery duration from cholecystectomy videos. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, pp. 586–593. Springer, Cham (2017)

    work page 2017

  7. [7]

    IEEE transactions on medical imaging (2018)

    Twinanda, A.P., Yengera, G., Mutter, D., Marescaux, J., Padoy, N.: Rsdnet: Learning to predict remaining surgery duration from laparoscopic videos without manual annotations. IEEE transactions on medical imaging (2018)

    work page 2018

  8. [8]

    In: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, pp

    Rivoir, D., Bodenstedt, S., Bechtolsheim, F., Distler, M., Weitz, J., Speidel, S.: Unsupervised temporal video segmentation as an auxiliary task for predicting the remaining surgery duration. In: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, pp. 29–37. Springer, Cham (2019) 14

    work page 2019

Show all 36 references
  1. [9]

    In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, pp

    Marafioti, A., Hayoz, M., Gallardo, M., M´ arquez Neila, P., Wolf, S., Zinkernagel, M., Sznitman, R.: Catanet: Predicting remaining cataract surgery duration. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, pp. 426–435. Springer, Cham (2021)

    2021

  2. [10]

    Computerized Medical Imaging and Graphics (2023)

    Wu, J., Zou, X., Tao, R., Zheng, G.: Nonlinear regression of remaining surgery duration from videos via bayesian lstm-based deep negative correlation learning. Computerized Medical Imaging and Graphics (2023)

    2023

  3. [11]

    arXiv preprint arXiv: 2409.16998 (2024)

    Wijekoon, A., Das, A., Herrera, R.R., Khan, D.Z., Hanrahan, J., Carter, E., Luoma, V., Stoyanov, D., Marcus, H.J., Bano, S.: Pitrsdnet: Predicting intra- operative remaining surgery duration in endoscopic pituitary surgery. arXiv preprint arXiv: 2409.16998 (2024)

    2024 arXiv

  4. [12]

    International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)

    Rivoir, D., Bodenstedt, S., Funke, I., Bechtolsheim, F., Distler, M., Weitz, J., Spei- del, S.: Rethinking anticipation tasks: Uncertainty-aware anticipation of sparse surgical instrument usage for context-aware assistance. International Conference on Medical Image Computing a...

    2020

  5. [13]

    Medical Image Analysis (2022)

    Yuan, K., Holden, M., Gao, S., Lee, W.: Anticipation for surgical workflow through instrument interaction and recognized signals. Medical Image Analysis (2022)

    2022

  6. [14]

    OpenAI blog (2019)

    Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog (2019)

    2019

  7. [15]

    IEEE International Conference on Computer Vision (2023) https://doi.org/10.1109/ICCV51070.2023.01271

    Wang, J., Chen, G., Huang, Y., Wang, L., Lu, T.: Memory-and-anticipation transformer for online action understanding. IEEE International Conference on Computer Vision (2023) https://doi.org/10.1109/ICCV51070.2023.01271

    2023

  8. [16]

    In: Medical Image Computing and Computer- Assisted Intervention – MICCAI 2010, pp

    Blum, T., Feußner, H., Navab, N.: Modeling and segmentation of surgical workflow from laparoscopic video. In: Medical Image Computing and Computer- Assisted Intervention – MICCAI 2010, pp. 400–407. Springer, Berlin, Heidelberg (2010)

    2010

  9. [17]

    PhD thesis, University of Strasbourg (2017)

    Twinanda, A.P.: Vision-based approaches for surgical activity recognition using laparoscopic and RBGD videos. PhD thesis, University of Strasbourg (2017)

    2017

  10. [18]

    Neural Information Processing Systems (2017)

    Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Neural Information Processing Systems (2017)

    2017

  11. [19]

    International Conference on Learning Representations (2020) 15

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learn...

    2020

  12. [20]

    In: Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C

    Gao, X., Jin, Y., Long, Y., Dou, Q., Heng, P.: Trans-svnet: Accurate phase recog- nition from surgical videos via hybrid embedding aggregation transformer. In: Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing an...

  13. [21]

    IEEE Trans

    Ding, X., Li, X.: Exploring segment-level semantics for online phase recognition from surgical videos. IEEE Trans. Medical Imaging 41(11), 3309–3319 (2022)

    2022

  14. [22]

    Medical Image Analysis 94, 103126 (2024) https://doi.org/10.1016/j.media.2024.103126

    Rivoir, D., Funke, I., Speidel, S.: On the pitfalls of batch normalization for end-to- end video learning: A study on surgical workflow analysis. Medical Image Analysis 94, 103126 (2024) https://doi.org/10.1016/j.media.2024.103126

    2024

  15. [23]

    International Con- ference on Medical Image Computing and Computer-Assisted Intervention (2024) https://doi.org/10.48550/arXiv.2408.03867

    Yang, S., Luo, L., Wang, Q., Chen, H.: Surgformer: Surgical transformer with hierarchical temporal attention for surgical phase recognition. International Con- ference on Medical Image Computing and Computer-Assisted Intervention (2024) https://doi.org/10.48550/arXiv.2408.03867

    2024 doi arXiv

  16. [24]

    IEEE Robotics and Automation Letters (2021) https://doi.org/10.1109/lra.2022.3156856

    Ban, Y., Rosman, G., Eckhoff, J., Ward, T.M., Hashimoto, D., Kondo, T., Iwaki, H., Meireles, O., Rus, D.: Supr-gan: Surgical prediction gan for event anticipa- tion in laparoscopic and robotic surgery. IEEE Robotics and Automation Letters (2021) https://doi.org/10.1109/lra.202...

    2021

  17. [25]

    arXiv preprint arXiv: 2403.06200 (2024)

    Boels, M., Liu, Y., Dasgupta, P., Granados, A., Ourselin, S.: Supra: Surgical phase recognition and anticipation for intra-operative planning. arXiv preprint arXiv: 2403.06200 (2024)

    2024 arXiv

  18. [26]

    IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (2022) https://doi.org/10.1109/ BHI56158.2022.9926801

    Zhang, X., Moubayed, N.A., Shum, H.P.H.: Towards graph representation learn- ing based surgical workflow anticipation. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (2022) https://doi.org/10.1109/ BHI56158.2022.9926801

    2022

  19. [27]

    arXiv preprint arXiv: 2402.01974 (2024)

    Yin, L., Ban, Y., Eckhoff, J., Meireles, O., Rus, D., Rosman, G.: Hypergraph- transformer (hgt) for interactive event prediction in laparoscopic and robotic surgery. arXiv preprint arXiv: 2402.01974 (2024)

    2024 arXiv

  20. [28]

    IEEE/RJS International Conference on Intelligent RObots and Systems (2020)

    Ginesi, M., Meli, D., Roberti, A., Sansonetto, N., Fiorini, P.: Autonomous task planning and situation awareness in robotic surgery. IEEE/RJS International Conference on Intelligent RObots and Systems (2020)

    2020

  21. [29]

    IEEE/RJS International Conference on Intelligent RObots and Systems (2020)

    Qin, Y., Feyzabadi, S., Allan, M., Burdick, J., Azizian, M.: davincinet: Joint prediction of motion and surgical state in robot-assisted surgery. IEEE/RJS International Conference on Intelligent RObots and Systems (2020)

    2020

  22. [30]

    IEEE Journal of Biomedical and Health Informatics (2023) 16

    Zhang, J., Zhou, S., Wang, Y., Shi, S., Wan, C., Zhao, H., Cai, X., Ding, H.: Laparoscopic image-based critical action recognition and anticipation with explainable features. IEEE Journal of Biomedical and Health Informatics (2023) 16

    2023

  23. [31]

    IEEE International Conference on Computer Vision (2021) https://doi.org/10.1109/ICCV48922

    Girdhar, R., Grauman, K.: Anticipative video transformer. IEEE International Conference on Computer Vision (2021) https://doi.org/10.1109/ICCV48922. 2021.01325

    2021

  24. [32]

    IEEE Trans

    Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., Mathelin, M., Padoy, N.: Endonet: A deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Medical Imaging 36(1), 86–97 (2017)

    2017

  25. [33]

    In: International Conference on Medi- cal Image Computing and Computer-Assisted Intervention, pp

    Wang, Z., Lu, B., Long, Y., Zhong, F., Cheung, T.-H., Dou, Q., Liu, Y.: Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy. In: International Conference on Medi- cal Image Computing and Computer-Assisted Interv...

    2022

  26. [34]

    IEEE Transactions on Biomedical Engineering (2025)

    Funke, I., Rivoir, D., Krell, S., Speidel, S.: Tunes: A temporal u-net with self-attention for video-based surgical phase recognition. IEEE Transactions on Biomedical Engineering (2025)

    2025

  27. [35]

    Jin, Y., Long, Y., Gao, X., Stoyanov, D., Dou, Q., Heng, P.-A.: Trans-svnet: hybrid embedding aggregation transformer for surgical workflow analysis. Inter- national Journal of Computer Assisted Radiology and Surgery (2022) 17 7 Supplementary Material 7.1 Implementation Detail...

    2022

  28. [2021]

    Springer, ??? (2021)

    2021

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