Search Results for author:
Found 11 papers, 4 papers with code
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
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.
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
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.
A systematic approach to deep learning-based nodule detection in chest radiographs
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.
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
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.
Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs
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.
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data
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 in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts.
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI
These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning.
Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs
However, for monitoring disease progression, \textit{lesion activity} in terms of new and enlarging lesions between two time points is a crucial biomarker.
4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently.
4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation
While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently.
