Search Results for author: Roland Opfer

Found 12 papers, 5 papers with code

Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection

1 code implementation21 Mar 2024 Finn Behrendt, Debayan Bhattacharya, Lennart Maack, Julia Krüger, Roland Opfer, Robin Mieling, Alexander Schlaefer

We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.

Segmentation SSIM +1

Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs

2 code implementations7 Dec 2023 Finn Behrendt, Debayan Bhattacharya, Robin Mieling, Lennart Maack, Julia Krüger, Roland Opfer, Alexander Schlaefer

Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI.

Anatomy Denoising +2

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

Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI

2 code implementations7 Mar 2023 Finn Behrendt, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer

The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets.

Anatomy Diversity +1

Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs

no code implementations17 Aug 2022 Finn Behrendt, Debayan Bhattacharya, Julia Krüger, Roland Opfer, Alexander Schlaefer

Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.

Multi-Label Classification

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

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

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

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