Search Results for author: Finn Behrendt

Found 16 papers, 7 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

PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM

1 code implementation18 Feb 2024 Debayan Bhattacharya, Konrad Reuter, Finn Behrendt, Lennart Maack, Sarah Grube, Alexander Schlaefer

Our primary novelty lies in PolypNextLSTM, which stands out as the leanest in parameters and the fastest model, surpassing the performance of five state-of-the-art image and video-based deep learning models.

Segmentation Video Segmentation +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

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

no code implementations31 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.

Anomaly Classification Classification

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

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

no code implementations1 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.

Unsupervised Anomaly Detection

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

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

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