Search Results for author: Marcel Bengs

Found 21 papers, 2 papers with code

A systematic approach to deep learning-based nodule detection in chest radiographs

1 code implementation Nature Scientific Reports 2023 Finn Behrendt, Marcel Bengs, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer

We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition.

Data Augmentation Lung Nodule Detection +3

Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus

no code implementations5 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.

Anomaly Classification Contrastive Learning

Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data

no code implementations12 Apr 2022 Finn Behrendt, Marcel Bengs, Frederik Rogge, Julia Krüger, Roland Opfer, Alexander Schlaefer

Overall, we highlight the importance of clean data sets for UAD in brain MRI and demonstrate an approach for detecting falsely labeled data directly during training.

Unsupervised Anomaly Detection

Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning

no code implementations11 Apr 2022 Maximilian Neidhardt, Marcel Bengs, Sarah Latus, Stefan Gerlach, Christian J. Cyron, Johanna Sprenger, Alexander Schlaefer

The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5. 01+-4. 37 kPa.

Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction

no code implementations31 Jan 2022 Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer

We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts.

Anatomy Lesion Detection +1

Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning

no code implementations14 Sep 2021 Marcel Bengs, Satish Pant, Michael Bockmayr, Ulrich Schüller, Alexander Schlaefer

Our top-performing method achieves the AUC-ROC value of 90. 90\% compared to 84. 53\% using the previous approach with smaller input tiles.

Classification Transfer Learning

Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets

no code implementations10 Sep 2021 Marcel Bengs, Michael Bockmayr, Ulrich Schüller, Alexander Schlaefer

In this work, we propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.

Transfer Learning

Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification

no code implementations2 Jul 2020 Marcel Bengs, Nils Gessert, Wiebke Laffers, Dennis Eggert, Stephan Westermann, Nina A. Mueller, Andreas O. H. Gerstner, Christian Betz, Alexander Schlaefer

We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models' ability to make use of the additional spectral information.

General Classification

Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

1 code implementation20 May 2020 Nils Gessert, Marcel Bengs, Matthias Schlüter, Alexander Schlaefer

Moreover, optical coherence tomography (OCT) and deep learning have been used for estimating forces based on deformation observed in volumetric image data.

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

no code implementations21 Apr 2020 Marcel Bengs, Stephan Westermann, Nils Gessert, Dennis Eggert, Andreas O. H. Gerstner, Nina A. Mueller, Christian Betz, Wiebke Laffers, Alexander Schlaefer

A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue.

A Deep Learning Approach for Motion Forecasting Using 4D OCT Data

no code implementations MIDL 2019 Marcel Bengs, Nils Gessert, Alexander Schlaefer

We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes.

Motion Compensation Motion Estimation +3

Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data

no code implementations21 Apr 2020 Marcel Bengs, Nils Gessert, Matthias Schlüter, Alexander Schlaefer

For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach.

Motion Estimation

4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation

no code implementations20 Apr 2020 Nils Gessert, Marcel Bengs, Julia Krüger, Roland Opfer, Ann-Christin Ostwaldt, Praveena Manogaran, Sven Schippling, Alexander Schlaefer

While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently.

Decoder Lesion Segmentation +1

Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models

no code implementations6 Nov 2019 Nils Gessert, Marcel Bengs, Alexander Schlaefer

As a result, we propose a recurrent model with state-max-pooling which automatically learns the relevance of different EIS measurements.

BIG-bench Machine Learning Lesion Classification

Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels

no code implementations12 Aug 2019 Nils Gessert, Martin Gromniak, Marcel Bengs, Lars Matthäus, Alexander Schlaefer

To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup.

EEG

Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images

no code implementations20 May 2019 Nils Gessert, Marcel Bengs, Lukas Wittig, Daniel Drömann, Tobias Keck, Alexander Schlaefer, David B. Ellebrecht

For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation.

General Classification Image Classification +1

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