1 code implementation • 29 Apr 2024 • Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Lennart Maack, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.
no code implementations • 31 Mar 2023 • Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.
no code implementations • 1 Nov 2022 • Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples.
no code implementations • 5 Sep 2022 • Debayan Bhattacharya, Benjamin Tobias Becker, Finn Behrendt, Marcel Bengs, Dirk Beyersdorff, Dennis Eggert, Elina Petersen, Florian Jansen, Marvin Petersen, Bastian Cheng, Christian Betz, Alexander Schlaefer, Anna Sophie Hoffmann
Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability.