CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning | Paper Citation Record

Observation 2117ba29-dd76-46d0-82d0-6a17c09db356 · outbound

This paper cites The Rise of Minimally Invasive Surgery: 16 Year Analysis of the Progres- sive Replacement of Open Surgery with Laparoscopy.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning The Rise of Minimally Invasive Surgery: 16 Year Analysis of the Progres- sive Replacement of Open Surgery with Laparoscopy

Reference 1

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Observation e840957b-2bbc-40dd-b3ee-f36ba8c2f330 · outbound

This paper cites Minimally Invasive Versus Open Lumbar Fusion: A Comparison of Blood Loss, Surgical Complications, and Hospital Course.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Minimally Invasive Versus Open Lumbar Fusion: A Comparison of Blood Loss, Surgical Complications, and Hospital Course

Reference 2

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Observation 1e707feb-d3bc-4f31-ba03-45f5c30d3b5d · outbound

This paper cites Minimally invasive spine surgery decreases postoperative pain and inflammation for patients with lumbar spinal stenosis.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Minimally invasive spine surgery decreases postoperative pain and inflammation for patients with lumbar spinal stenosis

Reference 3

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Observation abf04d50-8f7c-44c1-9f9c-ee3633988201 · outbound

This paper cites Impact of minimally invasive surgery on immune function and stress response in gastric cancer patients,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Impact of minimally invasive surgery on immune function and stress response in gastric cancer patients,

Reference 4

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This paper cites Does Minimally Invasive Mitral Valve Repair Mean Less Postoperative Pain? The Annals of Thoracic Surgery.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Does Minimally Invasive Mitral Valve Repair Mean Less Postoperative Pain? The Annals of Thoracic Surgery

Reference 5

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Observation d9179218-0874-4549-82ec-eb3ce6fa54d7 · outbound

This paper cites Artificial intelligence- based computer vision in surgery: Recent advances and future perspectives.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Artificial intelligence- based computer vision in surgery: Recent advances and future perspectives

Reference 6

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Observation 25af26d1-712d-4713-a02f-d9949f90754c · outbound

This paper cites Digital tools and innovative healthcare solutions: Serious games and gamification in surgical training and patient care,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Digital tools and innovative healthcare solutions: Serious games and gamification in surgical training and patient care,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:42.028193Z

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Observation 44b2a0fd-c657-46e0-a8f5-ec408c927e32 · outbound

This paper cites Computer Vi- sion in the Operating Room: Opportunities and Caveats,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Computer Vi- sion in the Operating Room: Opportunities and Caveats,

Reference 8

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Observation 0077143e-e4ba-4a2c-98a6-5254897f68c5 · outbound

This paper cites Novel applications of deep learning in surgical training,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Novel applications of deep learning in surgical training,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:54:42.018836Z

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This paper cites Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning- based framework.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning- based framework

Reference 10

Resolution: verified exact; doi, observed 2026-08-06T19:54:39.932588Z

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Observation 390df135-3634-4202-92a0-9bc6a6caa469 · outbound

This paper cites Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning

Reference 11

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Observation 8f1658a4-c4dd-4fd5-aff4-6cee07a33272 · outbound

This paper cites Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review,

Reference 12

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This paper cites S3Net: A Single Stream Structure for Depth Guided Image Relighting.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning S3Net: A Single Stream Structure for Depth Guided Image Relighting

Reference 13

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This paper cites MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation,

Reference 14

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This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 15

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Observation 90ea689a-faf3-4615-a24b-d5a35a4b2819 · outbound

This paper cites Segment Anything.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segment Anything

Reference 16

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Observation 68a3a034-4946-4916-8007-372c479b0fe2 · outbound

This paper cites Track Anything: Segment Anything Meets Videos.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Track Anything: Segment Anything Meets Videos

Reference 17

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Observation 8c1a4ef5-9eec-4c0d-9f44-d0b04046bd4a · outbound

This paper cites Personalize Segment Anything Model with One Shot.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Personalize Segment Anything Model with One Shot

Reference 18

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Observation 47166685-3f7f-4e81-b84a-36d59a0de644 · outbound

This paper cites SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

Reference 19

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Observation 8b2d5209-9f4b-4426-a4b5-b39643efa823 · outbound

This paper cites DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation

Reference 20

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This paper cites Segmentation of Brain Tumor in MRI Scans Using Deep Learning,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmentation of Brain Tumor in MRI Scans Using Deep Learning,

Reference 21

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This paper cites Object Segmentation for Robotic Grasping in Cluttered Environments,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Object Segmentation for Robotic Grasping in Cluttered Environments,

Reference 22

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This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 23

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This paper cites U-Net: Convolutional Net- works for Biomedical Image Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning U-Net: Convolutional Net- works for Biomedical Image Segmentation,

Reference 24

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This paper cites Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,

Reference 25

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 26

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This paper cites UNETR: Transformers for 3D Medical Image Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning UNETR: Transformers for 3D Medical Image Segmentation,

Reference 27

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This paper cites Segmenter: Transformer for Semantic Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmenter: Transformer for Semantic Segmentation,

Reference 28

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This paper cites SETR: Vision Transformers for Semantic Segmenta- tion,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SETR: Vision Transformers for Semantic Segmenta- tion,

Reference 29

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This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 30

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This paper cites Prostate Segmentation Using Hybrid Transformer-CNN Models,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Prostate Segmentation Using Hybrid Transformer-CNN Models,

Reference 31

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This paper cites SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,

Reference 32

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Observation a3ea7f50-0ca6-4116-ad0c-b6be99fe7bcb · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Learning Transferable Visual Models From Natural Language Supervision,

Reference 33

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Observation d2e2828d-c010-48b9-8b01-ecc11a1d32ab · outbound

This paper cites Zero-Shot Medical Image Segmentation Using CLIP,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Zero-Shot Medical Image Segmentation Using CLIP,

Reference 34

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This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 35

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This paper cites Vision-Language Models for Surgical Assistance: A Case Study in Laparoscopic Surgery,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Vision-Language Models for Surgical Assistance: A Case Study in Laparoscopic Surgery,

Reference 36

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning A Simple Framework for Contrastive Learning of Visual Representations

Reference 37

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CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 38

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This paper cites Bengio, J.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Bengio, J

Reference 39

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CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 40

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CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 41

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CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 42

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This paper cites Adam: A Method for Stochastic Optimization.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 43

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This paper cites 2017 Robotic Instrument Segmentation Challenge.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning 2017 Robotic Instrument Segmentation Challenge

Reference 44

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This paper cites 2018 Robotic Scene Segmentation Challenge.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning 2018 Robotic Scene Segmentation Challenge

Reference 45

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This paper cites ISINet: An Instance-Based Approach for Surgical Instrument Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning ISINet: An Instance-Based Approach for Surgical Instrument Segmentation

Reference 46

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This paper cites Available from: https://www.sciencedirect.com/science/article/pii/S0003497522014308.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Available from: https://www.sciencedirect.com/science/article/pii/S0003497522014308

Reference 1178

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