Amine Yamlahi

VLDB 2026 Research / reviewers in the wild / expert

dblp:322/2217 · DBLP profile ↗

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6ranked-venue papers

2as first author

6since 2021 · last 2026

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 5 · 2 first-author · 5 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Year	Publication	Venue	Position
2026	Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge abstract Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. Tobias Rueckert, David Rauber, Raphaela Maerkl, Leonard Klausmann, Suemeyye R. Yildiran, Max Gutbrod, Danilo Weber Nunes, Alvaro Fernandez Moreno, Imanol Luengo, Danail Stoyanov, Nicolas Toussaint, Enki Cho, Hyeon Bae Kim, Oh Sung Choo, Ka Young Kim, Seong Tae Kim 0001, Gonçalo Arantes, Kehan Song, Junchen Xiong, Tingyi Lin, Shunsuke Kikuchi, Hiroki Matsuzaki, Atsushi Kouno, João Renato Ribeiro Manesco, João Paulo Papa, Tae-Min Choi, Tae Kyeong Jeong, Oluwatosin Alabi, Tom Vercauteren, Runzhi Wu, Mengya Xu, An Wang 0007, Long Bai 0008, Hongliang Ren 0001, Amine Yamlahi, Jakob Hennighausen, Lena Maier-Hein, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Shu Yang 0004, Yihui Wang 0002, Hao Chen 0011, Santiago Rodríguez, Nicolás Aparicio, Leonardo Manrique, Juan Camilo Lyons, Olivia Hosie, Nicolás Ayobi, Pablo Andrés Arbeláez, Yiping Li 0002, Yasmina Alkhalil, Sahar Nasirihaghighi, Stefanie Speidel, Daniel Rueckert, Hubertus Feußner, Dirk Wilhelm, Christoph Palm	Medical Image Anal.	38
2025	Smarter Self-distillation: Optimizing the Teacher for Surgical Video Applications Amine Yamlahi, Piotr Kalinowski, Patrick Godau, Rayan Younis, Martin Wagner 0001, Beat P. Müller-Stich, Lena Maier-Hein	MICCAI (9)	1
2025	PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery abstract The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery, including: which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery or during live surgery. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a particularly challenging task when compared to other minimally invasive surgeries due to: the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. The top performing model for step recognition utilised a transformer based architecture, uniquely using an autoregressive decoder with a positional encoding input. The top performing model for instrument recognition utilised a spatial encoder followed by a temporal encoder, which uniquely used a 2-layer temporal architecture. In both cases, these models outperformed purely spatial based models, illustrating the importance of sequential and temporal information. This PitVis-2023 therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686. Adrito Das, Danyal Z. Khan, Dimitris Psychogyios, John G. Hanrahan, Francisco Vasconcelos 0001, You Pang, Zhen Chen 0018, Jinlin Wu, Xiaoyang Zou, Guoyan Zheng, Abdul Qayyum 0002, Moona Mazher, Muhammad Imran Razzak, Tianbin Li, Jin Ye 0002, Junjun He, Szymon Plotka, Joanna Kaleta, Amine Yamlahi, Antoine Jund, Patrick Godau, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Dominik Rivoir, Stefanie Speidel, Alejandra Pérez, Santiago Rodríguez, Pablo Andrés Arbeláez, Danail Stoyanov, Hani J. Marcus, Sophia Bano	Medical Image Anal.	20
2023	Why is the Winner the Best? abstract International 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	58
2023	Self-distillation for Surgical Action Recognition Amine Yamlahi, Thuy Nuong Tran, Patrick Godau, Melanie Schellenberg, Dominik Michael, Finn-Henri Smidt, Jan-Hinrich Nölke, Tim Adler, Minu Tizabi, Chinedu Innocent Nwoye, Nicolas Padoy, Lena Maier-Hein	MICCAI (9)	1
2023	CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection Chinedu Innocent Nwoye, Tong Yu 0009, Saurav Sharma, Aditya Murali, Deepak Alapatt, Armine Vardazaryan, Kun Yuan 0004, Jonas Hajek, Wolfgang Reiter, Amine Yamlahi, Finn-Henri Smidt, Xiaoyang Zou, Guoyan Zheng, Bruno Oliveira 0002, Helena R. Torres, Satoshi Kondo, Satoshi Kasai, Felix Holm, Ege Özsoy, Shuangchun Gui, Sista Raviteja, Rachana Sathish, Pranav Poudel, Binod Bhattarai, Ziheng Wang 0003, Guo Rui, Melanie Schellenberg, João L. Vilaça, Tobias Czempiel, Zhenkun Wang 0001, Debdoot Sheet, Shrawan Kumar Thapa, Max Berniker, Patrick Godau, Pedro Morais, Sudarshan Regmi, Thuy Nuong Tran, Jaime C. Fonseca 0001, Jan-Hinrich Nölke, Estevão Lima, Eduard Vazquez, Lena Maier-Hein, Nassir Navab, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Didier Mutter, Nicolas Padoy	Medical Image Anal.	10