Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation | Paper Citation Record

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Unresolved cited work

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Artificial intelligence and surgical decision-making

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Surgical data science–from concepts toward clinicaltranslation.Medicalimageanalysis,2022,76:102306

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This paper cites 2018 Robotic Scene Segmentation Challenge.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation 2018 Robotic Scene Segmentation Challenge

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This paper cites Association of surgical skill assessment with clinical outcomesincancersurgery.JAMAsurgery,2020,155(7):590-598.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Association of surgical skill assessment with clinical outcomesincancersurgery.JAMAsurgery,2020,155(7):590-598

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Towards unified surgical skill assessment

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Masked-attention mask transformer for universal image segmentation

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This paper cites Pseudo-label guided cross-video pixel contrast for robotic surgical scene segmentation with limited annotations.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Pseudo-label guided cross-video pixel contrast for robotic surgical scene segmentation with limited annotations

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This paper cites Pixel-wise contrastive learning for multi-class instrument segmentation in endoscopic robotic surgery videos using dataset-wide sample queues[J].

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Pixel-wise contrastive learning for multi-class instrument segmentation in endoscopic robotic surgery videos using dataset-wide sample queues[J]

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This paper cites International Journal of Computer Assisted Radiology andSurgery,2022,17(10):1903-1913.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation International Journal of Computer Assisted Radiology andSurgery,2022,17(10):1903-1913

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This paper cites Deep learning approach for bubble segmentation from hysteroscopicimages.Medical&BiologicalEngineering&Computing,2022,60(6):1613-1626.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Deep learning approach for bubble segmentation from hysteroscopicimages.Medical&BiologicalEngineering&Computing,2022,60(6):1613-1626

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Observation cfc0f05a-3a65-4221-9cdc-7045f03124a0 · outbound

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Unresolved cited work

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation

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This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.2024:1081-1093.

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.2024:1081-1093

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation DB-SAM: Delving into High Quality Universal Medical Image Segmentation

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Medclip: Contrastive learning from unpaired medical images and text[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.2022:3876-3887

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Unet++: A nested u-net architecture for medicalimagesegmentation.DeepLearninginMedicalImageAnalysisandMultimodalLearning forClinicalDecisionSupport,Springer(2018),pp.3-11

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation nnU-Net: a self-configuring method for deep learning- basedbiomedicalimagesegmentation[J].Naturemethods,2021,18(2):203-211

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Segment anything

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Segment anything in medical images[J]

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Surgicalsam: Efficient class promptable surgical instrument segmentation[C]//Proceedings of the AAAI Conference on Artificial Intelligence

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Med-VLM: Enhancing Medical Image Segmentation Accuracy through Vision-Language Model[C]//Proceedings of the IEEE/CVF International Conference on ComputerVision.2025:7283-7293

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Automated detection of endometrial polyps from hysteroscopic videosusingdeeplearning[J].Diagnostics,2023,13(8):1409

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Digital image analysis with fully connected convolutional neural network to facilitate hysteroscopic fibroid resection[J]

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Exploring intra-and inter-video relation for surgical semantic scene segmentation.IEEETransactionsonMedicalImaging,2022,41(11):2991-3002

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Visual instruction tuning

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Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation Text promptable surgical instrument segmentation with vision- languagemodels[J].AdvancesinNeuralInformationProcessingSystems,2023,36:28611-28623

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