19 citations · 32 across the 7 of their papers we have counts for
7 papers
Sterilizable Scene Graph Generation for Operating Rooms
Nick Lemke, Ssharvien Kumar Sivakumar, Antoine P. Sanner +4
Scene graph generation from surgical video enables a holistic and structured understanding of surgical scenes by modeling objects and their semantic relationships. Despite recent a…
Federated Voxel Scene Graph for Intracranial Hemorrhage
Antoine P. Sanner, Jonathan Stieber, Nils F. Grauhan +4
Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are star…
Detection of Intracranial Hemorrhage for Trauma Patients
Antoine P. Sanner, Nils F. Grauhan, Marc A. Brockmann +2
Whole-body CT is used for multi-trauma patients in the search of any and all injuries. Since an initial assessment needs to be rapid and the search for lesions is done for the whol…
Voxel Scene Graph for Intracranial Hemorrhage
Antoine P. Sanner, Nils F. Grauhan, Marc A. Brockmann +2
Patients with Intracranial Hemorrhage (ICH) face a potentially life-threatening condition, and patient-centered individualized treatment remains challenging due to possible clinica…
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models
Yannik Frisch, Moritz Fuchs, Antoine Sanner +7
Cataract surgery is a frequently performed procedure that demands automation and advanced assistance systems. However, gathering and annotating data for training such systems is re…
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang +31
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores…
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
Antoine Sanner, Camila Gonzalez, Anirban Mukhopadhyay
The recent achievements of Deep Learning rely on the test data being similar in distribution to the training data. In an ideal case, Deep Learning models would achieve Out-of-Distr…