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Found 37 papers, 18 papers with code
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% 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%.
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)
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition.
Live Laparoscopic Video Retrieval with Compressed Uncertainty
Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care.
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation
We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties.
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets.
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.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
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
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room
Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to a statistically different unlabeled target domain.
Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety
Minimally invasive image-guided surgery heavily relies on vision.
Intraoperative time out to promote the implementation of the critical view of safety in laparoscopic cholecystectomy: a video-based assessment of 343 procedures
The primary endpoint was to compare the rate of CVS achievement between LCs performed in the year before and the year after the 5-second rule.
A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images
3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery.
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Encode the Unseen: Predictive Video Hashing for Scalable Mid-Stream Retrieval
This paper tackles a new problem in computer vision: mid-stream video-to-video retrieval.
Artificial Intelligence in Surgery: Neural Networks and Deep Learning
Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology.
Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images
Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR).
Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation
2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room that can analyze and support the clinical activities.
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
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice.
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 #1 on
Surgical tool detection
on Cholec80
Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior.
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
Methods: We propose a comparison of 6 state-of-the-art face detectors on clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of operating room images capturing real surgical activities.
Future-State Predicting LSTM for Early Surgery Type Recognition
This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video.
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods.
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.
Ranked #3 on
Surgical tool detection
on Cholec80
Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos.
A generalizable approach for multi-view 3D human pose regression
As 2D poses are collected at test time using a SV pose detector, which might generate inaccurate detections, we model its characteristics and incorporate this information during training.
RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos.
A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features.
Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016
On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines.
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos.
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room.
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
Ranked #4 on
Surgical tool detection
on Cholec80
