14 citations · 31 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
Deep-Learning-Assisted Analysis of Cataract Surgery Videos
Negin Ghamsarian
Following the technological advancements in medicine, the operation rooms are evolving into intelligent environments. The context-aware systems (CAS) can comprehensively interpret…
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception
Negin Ghamsarian, Sebastian Wolf, Martin Zinkernagel +2
Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and…
Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning
Negin Ghamsarian, Doris Putzgruber-Adamitsch, Stephanie Sarny +3
A critical yet unpredictable complication following cataract surgery is intraocular lens dislocation. Postoperative stability is imperative, as even a tiny decentration of multifoc…
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…
Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training
Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila +4
Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In…