Dr. Dennis Eggert Profile

Dr. Dennis Eggert

at Universitätsklinikum Hamburg-Eppendorf

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Publications (8)

Proceedings Article | 3 April 2024 Presentation + Paper

Convolutional transformer network for paranasal anomaly classification in the maxillary sinus

Debayan Bhattacharya, Finn Behrendt, Lennart Maack, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer

Proceedings Volume 12927, 1292717 (2024) https://doi.org/10.1117/12.3005515

KEYWORDS: Transformers, Magnetic resonance imaging, Head, Education and training, Deep learning, Convolution, Polyps, Neck, Feature selection, Cross validation

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Proceedings Article | 29 March 2024 Presentation + Paper

Motion-compensated OCT imaging of laryngeal tissue

Sarah Latus, Marica Kulas, Johanna Sprenger, Debayan Bhattacharya, Philippe Breda, Lukas Wittig, Tim Eixmann, Gereon Hüttmann, Lennart Maack, Dennis Eggert, Christian Betz, Alexander Schlaefer

Proceedings Volume 12928, 1292809 (2024) https://doi.org/10.1117/12.3006729

KEYWORDS: Optical coherence tomography, Tissues, Endoscopy, In vivo imaging, Image filtering, Visualization, Image analysis, Motion analysis, Detection and tracking algorithms

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Proceedings Article | 7 April 2023 Presentation + Paper

Unsupervised anomaly detection of paranasal anomalies in the maxillary sinus

Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer

Proceedings Volume 12465, 124651B (2023) https://doi.org/10.1117/12.2651525

KEYWORDS: Magnetic resonance imaging, Education and training, Polyps, Head, Neck, Machine learning, Data modeling, Voxels, Medical image reconstruction, Image restoration, Convolutional neural networks

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Proceedings Article | 15 March 2023 Presentation

Endoscopic multiphoton fluorescence lifetime imaging of NADH (Conference Presentation)

Julius Heitz, Lukas Braun, Dennis Eggert, Hauke Studier, Wolfgang Becker, Christian Betz

Proceedings Volume PC12384, PC123840W (2023) https://doi.org/10.1117/12.2666398

KEYWORDS: Nicotinamide adenine dinucleotide, Fluorescence lifetime imaging, Endoscopy, Multiphoton fluorescence microscopy, Tissues, Autofluorescence, Mode conditioning cables, Endoscopes, Time correlated single photon counting, Technology

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Proceedings Article | 4 April 2022 Presentation + Paper

Self-supervised U-Net for segmenting flat and sessile polyps

Debayan Bhattacharya, Christian Betz, Dennis Eggert, Alexander Schlaefer

Proceedings Volume 12033, 120333F (2022) https://doi.org/10.1117/12.2611364

KEYWORDS: Image segmentation, Data modeling, Performance modeling, Cancer, Medical imaging, Computer aided diagnosis and therapy, Colorectal cancer, Binary data, Visualization, Video processing, Machine learning, Convolutional neural networks, Artificial neural networks

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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 cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Manual inspection can cause misdetections because polyps vary in color, shape, size and appearance. To this end, Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos. The system acts a secondary check to help clinicians reduce misdetections so that polyps may be resected before they transform to cancer. Polyps vary in color, shape, size, texture and appearance. As a result, the miss rate of polyps is between 6% and 27% despite the prominence of CADx solutions. Furthermore, sessile and flat polyps which have diameter less than 10 mm are more likely to be undetected. Convolutional Neural Networks(CNN) have shown promising results in polyp segmentation. However, all of these works have a supervised approach and are limited by the size of the dataset. It was observed that smaller datasets reduce the segmentation accuracy of ResUNet++. Self-supervision is a stronger alternative to fully supervised learning especially in medical image analysis since it redresses the limitations posed by small annotated datasets. From the self-supervised approach proposed by Jamaludin et al., it is evident that pretraining a network with a proxy task helps in extracting meaningful representations from the underlying data which can then be used to improve the performance of the final downstream supervised task. In summary, we train a U-Net to inpaint randomly dropped out pixels in the image as a proxy task. The dataset we use for pretraining is Kvasir-SEG dataset. This is followed by a supervised training on the limited Kvasir-Sessile dataset. Our experimental results demonstrate that with limited annotated dataset and a larger unlabeled dataset, self-supervised approach is a better alternative than fully supervised approach. Specifically, our self-supervised U-Net performs better than five segmentation models which were trained in supervised manner on the Kvasir-Sessile dataset.

Proceedings Article | 16 March 2020 Presentation + Paper

Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection

Marcel Bengs, Stephan Westermann, Nils Gessert, Dennis Eggert, Andreas O. Gerstner, Nina Mueller, Christian Betz, Wiebke Laffers, Alexander Schlaefer

Proceedings Volume 11314, 113141L (2020) https://doi.org/10.1117/12.2549251

KEYWORDS: In vivo imaging, Tumors, Tissues, Cancer, Convolution, Neck, Head, Tissue optics, Image quality, Diagnostics

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Proceedings Article | 6 March 2020 Presentation

In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (Conference Presentation)

Dennis Eggert, Marcel Bengs, Stephan Westermann, Nils Gessert, Andreas O. Gerstner, Nina Müller, Alexander Schlaefer, Christian Betz, Wiebke Laffers

Proceedings Volume 11213, 112130L (2020) https://doi.org/10.1117/12.2557496

KEYWORDS: In vivo imaging, Hyperspectral imaging, Cancer, Tumors, Satellites, Surgery, Tissues, Convolutional neural networks, Convolution

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Proceedings Article | 19 February 2020 Presentation + Paper

Differentiation of tumors of the upper respiratory tract using optical metabolic imaging

Dennis Eggert, Volkan Dogan, David Gaertner, Christian Betz

Proceedings Volume 11213, 112130K (2020) https://doi.org/10.1117/12.2557361

KEYWORDS: Tissues, Fluorescence lifetime imaging, Tumors, Tissue optics, Optical imaging, Microscopes, Surgery, Cancer

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Showing 5 of 8 publications

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