Amandine Gout
Papers
2
Total Citations
13
H-Index
2
About
Amandine Gout is a researcher whose work bridges medical imaging, computer vision, and robotics, with a focus on practical, real-world applications. Her primary research areas include domain adaptation for 3D reconstruction, surgical navigation, and scene representation under varying environmental conditions. Gout’s most impactful contribution is her 2024 study on domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data, which has garnered 10 citations. This work directly addresses critical barriers to the adoption of surgical navigation in orthopedics, such as time constraints, cost, radiation exposure, and workflow integration, building on her earlier X23D framework. Her 2017 study on evaluating off-the-shelf CNNs for representing natural scenes with large seasonal variations, though less cited (3 citations), demonstrates her versatility in tackling challenges in robotics and environmental perception. Gout’s research is notable for its translational potential, aiming to make advanced surgical technologies more accessible and efficient. Her work reflects a commitment to solving practical problems at the intersection of AI and healthcare, making her a promising voice in the field of medical computer vision.
Research Focus
Key Achievements
Top Papers
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- 2