Search Results for author:
Found 17 papers, 10 papers with code
CoSimGen: Controllable Diffusion Model for Simultaneous Image and Mask Generation
Existing generative models often target single-modality outputs, either images or segmentation masks, failing to address the need for high-quality, simultaneous image-mask generation.
SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation
Acquiring and annotating surgical data is often resource-intensive, ethical constraining, and requiring significant expert involvement.
Surgical Text-to-Image Generation
Human expert survey shows that participants were highly challenged by the realistic characteristics of the generated samples, demonstrating Surgical Imagen's effectiveness as a practical alternative to real data collection.
SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos
This work sets a new standard in surgical tool tracking, providing dynamic trajectories for more adaptable and precise assistance in minimally invasive surgeries.
Ranked #1 on
Visibility Tracking
on CholecTrack20
CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
Tool tracking in surgical videos is vital in computer-assisted intervention for tasks like surgeon skill assessment, safety zone estimation, and human-machine collaboration during minimally invasive procedures.
Ranked #1 on
Object Tracking
on 1
Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions
We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition
Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition.
Ranked #2 on
Action Triplet Recognition
on CholecT45 (cross-val)
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7. 4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%.
Ranked #1 on
Semantic Segmentation
on Endoscapes
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
We also develop a metrics library, ivtmetrics, for model evaluation on surgical triplets.
Ranked #1 on
Action Triplet Recognition
on CholecT45
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #2 on
Surgical phase recognition
on HeiChole Benchmark
Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.
Ranked #1 on
Action Triplet Recognition
on CholecT50
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
Ranked #1 on
Action Triplet Recognition
on CholecT40
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
Ranked #2 on
Surgical tool detection
on Cholec80
