Demo Data from SIGIR 2026 / reviewers in the wild / expert
Maria Grammatikopoulou
dblp:190/8534
· DBLP profile ↗
9ranked-venue papers
6as first author
3since 2021 · last 2023
0009-0002-8345-0850ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 5 first-author · 1 since 2021Systems, architecture and hardware · 6 · 5 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
4 papers |
Robot manipulation · 50% Deep learning architectures and training · 25% Motion planning and robot control · 14% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Human-computer interaction and pervasive computing
2 papers |
Human-robot interaction · 72% Haptics and multimodal interaction · 28% |
Topics — the 9 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › medical imaging
medical image analysis |
0.7 | 1 | 2023 | Why is the Winner the Best? · CVPR 2023 |
Robotics › Motion planning and robot control
active constraints |
0.4 | 1 | 2019 | Active Contraints for Tool-Shaft Collision Avoidance in Minimally Invasive Surgery · ICRA 2019 |
Robotics › Robot manipulation › medical robotics › medical robot control
surgical robot control |
0.4 | 1 | 2019 | Active Contraints for Tool-Shaft Collision Avoidance in Minimally Invasive Surgery · ICRA 2019 |
Robotics › Robot manipulation › micromanipulation
microassembly |
0.3 | 1 | 2017 | Gaze contingent control for optical micromanipulation · ICRA 2017 |
Robotics › Robot manipulation › micromanipulation
optical tweezers |
0.3 | 1 | 2017 | Gaze contingent control for optical micromanipulation · ICRA 2017 |
Computer vision › 3D vision › pose estimation
orientation estimation |
0.3 | 1 | 2017 | Gaze contingent control for optical micromanipulation · ICRA 2017 |
Robotics › Robot manipulation
grasping |
0.2 | 1 | 2014 | A controller for stable grasping and desired finger shaping without contact sensing · ICRA 2014 |
Robotics › Robot manipulation › grasping › grasp control
stable grasp control |
0.2 | 1 | 2014 | A controller for stable grasping and desired finger shaping without contact sensing · ICRA 2014 |
Haptics and multimodal interaction › haptic feedback
haptic guidance |
0.1 | 1 | 2019 | Active Contraints for Tool-Shaft Collision Avoidance in Minimally Invasive Surgery · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 1.3elasto-plastic frictional force control · 0.8haptic constraints · 0.6eye fixation tracking · 0.6simulation · 0.2control synthesis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Why is the Winner the Best?abstractInternational benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). 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. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein |
CVPR | 77 |
| 2021 | Detection of Critical Structures in Laparoscopic Cholecystectomy Using Label Relaxation and Self-supervision
David Owen 0001, Maria Grammatikopoulou, Imanol Luengo, Danail Stoyanov |
MICCAI (4) | 2 |
| 2021 | CaDIS: Cataract dataset for surgical RGB-image segmentationabstractVideo feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/. Maria Grammatikopoulou, Evangello Flouty, Abdolrahim Kadkhodamohammadi, Gwenolé Quellec, Andre Chow, Jean Nehme, Imanol Luengo, Danail Stoyanov |
Medical Image Anal. | 1 |
| 2019 | Active Contraints for Tool-Shaft Collision Avoidance in Minimally Invasive SurgeryabstractRecent advances in teleoperation-based robotic-assisted Minimally Invasive Surgery (MIS) have made significant inroads in clinical adoption. However, such master-slave surgical systems create a physical separation between the surgeon and the patient. The concept of Active Constraints (ACs) provides guidance and sensory information to surgical robot operators in a form of haptic, visual or audible cues. This work proposes a novel ACs approach to avoid surgical tool-clashing and collision of the tool-shaft with delicate anatomy using elasto-plastic frictional force control. The presented framework is designed to reduce the occurence of direct coupling during electrocautery and to protect high-risk regions in Minimally Invasive Partial Nephrectomy (MIPN). Moreover, we combine aforementioned ACs methods and propose a solution when simultaneous penetration of both constraints occurs. The proposed methodology is implemented on the teleoperated da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its performance is demonstrated through three types of user experiments. The experimental results show that the developed algorithms are of significant benefit in performing the tasks with ACs assistance. Artur Banach, Konrad Leibrandt, Maria Grammatikopoulou, Guang-Zhong Yang |
ICRA | 3 |
| 2018 | Depth Estimation of Optically Transparent Microrobots Using Convolutional and Recurrent Neural NetworksabstractEstimating the three-dimensional (3D) position of microrobots is necessary in order to develop closed-loop control techniques and to improve the user's 3D perception in the micro-scale. This paper describes a depth estimation method based on supervised learning for optically transparent microrobots of known geometry. The proposed methodology uses Convolutional Neural Networks (CNNs) combined with a Recurrent Network, in particular a Long Short-Term Memory (LSTM) cell for depth regression. The proposed depth regression model is independent of the 3D orientation of the microrobot and is robust to varying illumination levels while it uses learned data-specific features. The model is trained and validated using microscope images and ground truth data generated from 3D-printed microrobots imaged in an Optical Tweezers (OT) setup. The validation results demonstrate that the proposed trained model can reconstruct the depth of the microrobot independently of its 3D orientation with submicron accuracy for the test set. Maria Grammatikopoulou, Lin Zhang 0021, Guang-Zhong Yang |
IROS | 1 |
| 2017 | Gaze contingent control for optical micromanipulationabstractOptical Tweezers (OT) have the advantage of non-contact interaction with target objects such as cells, overcoming the pitfall of obstructive adhesion forces which are present in contact micromanipulation. It is also feasible to manipulate a number of small microparts simultaneously or 3D structures by using multiple laser traps. These capabilities give rise to the potential to develop a human-robot interface to facilitate microassembly tasks. This paper presents a gaze contingent control framework and a method for 3D orientation estimation for optical micromanipulation. The proposed strategy aims to use OT as an interactive microassembly platform. The framework comprises I) a strategy to recognize the operator's intentions in order to interactively place and reconfigure the optical traps using the operator's eye fixation point, II) haptic constraints generated from the user's eye gaze to assist positioning of the assembled microparts and III) a method for 3D orientation estimation. The performance of the proposed framework is assessed through a set of experiments comparing it to the standard OT user interface. Three-dimensional manipulation and orientation estimation of a non-spherical microstructure are also performed. Maria Grammatikopoulou, Guang-Zhong Yang |
ICRA | 1 |
| 2017 | Depth estimation of optically transparent laser-driven microrobotsabstractSix degree-of-freedom (DoF) pose feedback is essential for the development of closed-loop control techniques for microrobotics. This paper presents two methods for depth estimation of transparent microrobots inside an Optical Tweezers (OT) setup using image sharpness measurements and model-based tracking. The x-y position and the 3D orientation of the object are estimated using online model-based template matching. The proposed depth estimation methodologies are validated experimentally by comparing the results with the ground truth. Maria Grammatikopoulou, Lin Zhang 0021, Guang-Zhong Yang |
IROS | 1 |
| 2016 | Motor channelling for safe and effective dynamic constraints in Minimally Invasive SurgeryabstractMotor channelling is a concept to provide navigation and sensory feedback to operators in master-slave surgical setups. It is beneficial since the introduction of robotic surgery creates a physical separation between the surgeon and patient anatomy. Active Constraints/Virtual Fixtures are proposed which integrate Guidance and Forbidden Region Constraints into a unified control framework. The developed approach provides guidance and safe manipulation to improve precision and reduce the risk of inadvertent tissue damage. Online three-degree-of-freedom motion prediction and compensation of the target anatomy is performed to complement the master constraints. The presented Active Constraints concept is applied to two clinical scenarios; surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery. The proposed motor channelling control strategy is implemented on the da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its effectiveness is demonstrated through a detailed user study. Maria Grammatikopoulou, Konrad Leibrandt, Guang-Zhong Yang |
IROS | 1 |
| 2014 | A controller for stable grasping and desired finger shaping without contact sensingabstractThis paper proposes a controller for the stable grasp of an arbitrary-shaped object on the horizontal plane by two robotic fingers with rigid hemispherical fingertips. The controller stabilizes the grasp with optimal force angles and desired finger shaping determined through the choice of a control constant without requiring the utilization of any contact information regarding contact locations and contact angles or any estimates of them. Simulation results demonstrate the performance of the proposed controller and show its clear advantages with respect to other known control schemes. Maria Grammatikopoulou, Efi Psomopoulou, Leonidas Droukas, Zoe Doulgeri |
ICRA | 1 |