2 citations · 2 across the 5 of their papers we have counts for
9 papers
Automated Estimation of Anatomical Risk Metrics for Endoscopic Sinus Surgery Using Deep Learning
Konrad Reuter, Lennart Thaysen, Bilkay Doruk +7
Endoscopic sinus surgery requires careful preoperative assessment of the skull base anatomy to minimize risks such as cerebrospinal fluid leakage. Anatomical risk scores like the K…
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +9
Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised…
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
Debayan Bhattacharya, Sarah Latus, Finn Behrendt +4
Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Th…
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +8
Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult…
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +8
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techn…
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Benjamin Tobias Becker, Finn Behrendt +10
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume…