12 citations · 20 across the 11 of their papers we have counts for
11 papers
SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
Sahar Nasirihaghighi, Negin Ghamsarian, Yiping Li +3
Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (F…
Data-Efficient Learning for Generalizable Surgical Video Understanding
Sahar Nasirihaghighi
Advances in surgical video analysis are transforming operating rooms into intelligent, data-driven environments. Computer-assisted systems support full surgical workflow, from preo…
diveXplore at the Video Browser Showdown 2024
Klaus Schoeffmann, Sahar Nasirihaghighi
According to our experience from VBS2023 and the feedback from the IVR4B special session at CBMI2023, we have largely revised the diveXplore system for VBS2024. It now integrates O…
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
Tobias Rueckert, David Rauber, Raphaela Maerkl +58
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…
GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset
Sahar Nasirihaghighi, Negin Ghamsarian, Leonie Peschek +4
Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative dec…
SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition
Yiping Li, Ronald de Jong, Sahar Nasirihaghighi +8
Accurate surgical phase recognition is crucial for computer-assisted interventions and surgical video analysis. Annotating long surgical videos is labor-intensive, driving research…
Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation
Negin Ghamsarian, Sahar Nasirihaghighi, Klaus Schoeffmann +1
Semi-supervised learning leverages unlabeled data to enhance model performance, addressing the limitations of fully supervised approaches. Among its strategies, pseudo-supervision…
Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition
Sahar Nasirihaghighi, Negin Ghamsarian, Raphael Sznitman +1
Accurate surgical phase recognition is crucial for advancing computer-assisted interventions, yet the scarcity of labeled data hinders training reliable deep learning models. Semi-…
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection
Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi +5
In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting i…
Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers
Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein +1
Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction…
Action Recognition in Video Recordings from Gynecologic Laparoscopy
Sahar Nasirihaghighi, Negin Ghamsarian, Daniela Stefanics +2
Action recognition is a prerequisite for many applications in laparoscopic video analysis including but not limited to surgical training, operation room planning, follow-up surgery…