2 citations · 2 across the 2 of their papers we have counts for
4 papers
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…
Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
Debayan Bhattacharya, Christian Betz, Dennis Eggert +1
Colorectal Cancer(CRC) poses a great risk to public health. It is the third most common cause of cancer in the US. Development of colorectal polyps is one of the earliest signs of…
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Nils Gessert, Matthias Schlüter, Sarah Latus +3
Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is…