Search Results for author:
Found 23 papers, 10 papers with code
One model to use them all: Training a segmentation model with complementary datasets
In this work, we propose a method to combine multiple partially annotated datasets, which provide complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
Graph data modelling for outcome prediction in oropharyngeal cancer patients
Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction.
Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography
To overcome the lack of labeled training data, a fast and effective generation pipeline in Blender3D is designed to simulate middle ear shapes and extract in-vivo noisy and partial point clouds.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
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)
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
Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance
We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations.
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
Image-based tracking of medical instruments is an integral part of surgical data science applications.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration
Using our unsupervised method as an auxiliary task for RSD training, we outperform other self-supervised methods and are comparable to the supervised state-of-the-art.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video
However, surgical gesture recognition based only on video is a challenging problem that requires effective means to extract both visual and temporal information from the video.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.
Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data
During laparoscopic interventions, the endoscopic video stream is an ideal source of such information.
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis
For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite.
Real-time image-based instrument classification for laparoscopic surgery
On manually provided bounding boxes, we achieve a instrument type recognition rate of up to 58% and on automatically detected bounding boxes up to 49%.
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
To achieve this, methods for surgical workflow analysis are crucial.
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery.
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task.
Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis
In this paper, we address this problem by presenting an unsupervised method for training a convolutional neural network (CNN) to differentiate between laparoscopic video frames on a temporal basis.
