62 citations · 138 across the 15 of their papers we have counts for
16 papers
SR-RKAC: Improving Single Image Defocus Deblurring
Peng Tang, Zhiqiang Xu, Pengfei Wei +5
We propose an efficient deep learning method for single image defocus deblurring (SIDD) by further exploring inverse kernel properties. Although the current inverse kernel method,…
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
Alessandro Wollek, Philip Haitzer, Thomas Sedlmeyr +5
Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, train…
Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images
Peng Tang, Yang Nan, Tobias Lasser
Many skin lesion analysis (SLA) methods recently focused on developing a multi-modal-based multi-label classification method due to two factors. The first is multi-modal data, i.e.…
Runtime optimization of acquisition trajectories for X-ray computed tomography with a robotic sample holder
Erdal Pekel, María Lancho Lavilla, Franz Pfeiffer +1
Tomographic imaging systems are expected to work with a wide range of samples that house complex structures and challenging material compositions, which can influence image quality…
German CheXpert Chest X-ray Radiology Report Labeler
Alessandro Wollek, Sardi Hyska, Thomas Sedlmeyr +5
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports. An automatic label extra…
Higher Chest X-ray Resolution Improves Classification Performance
Alessandro Wollek, Sardi Hyska, Bastian Sabel +2
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons. However, chest X-rays are acquired at a m…
WindowNet: Learnable Windows for Chest X-ray Classification
Alessandro Wollek, Sardi Hyska, Bastian Sabel +2
Chest X-ray (CXR) images are commonly compressed to a lower resolution and bit depth to reduce their size, potentially altering subtle diagnostic features. Radiologists use windowi…
Spherical acquisition trajectories for X-ray computed tomography with a robotic sample holder
Erdal Pekel, Martin Dierolf, Franz Pfeiffer +1
This work presents methods for the seamless execution of arbitrary spherical trajectories with a seven-degree-of-freedom robotic arm as a sample holder. The sample holder is integr…
Transformer-based interpretable multi-modal data fusion for skin lesion classification
Theodor Cheslerean-Boghiu, Melia-Evelina Fleischmann, Theresa Willem +1
A lot of deep learning (DL) research these days is mainly focused on improving quantitative metrics regardless of other factors. In human-centered applications, like skin lesion cl…
Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification
Alessandro Wollek, Robert Graf, Saša Čečatka +4
Purpose: To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency using the example of pneumot…
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Tina Dorosti, Manuel Schultheiss, Felix Hofmann +7
We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by explorin…
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
Alessandro Wollek, Theresa Willem, Michael Ingrisch +2
To investigate the impact of OOD radiographs on existing chest X-ray classification models and to increase their robustness against OOD data. The study employed the commonly used c…
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
Theodor Cheslerean-Boghiu, Felix C. Hofmann, Manuel Schultheiß +3
Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been…
Task-specific Performance Prediction and Acquisition Optimization for Anisotropic X-ray Dark-field Tomography
Theodor Cheslerean-Boghiu, Franz Pfeiffer, Tobias Lasser
Anisotropic X-ray Dark-field Tomography (AXDT) is a recently developed imaging modality that enables the visualization of oriented microstructures using lab-based X-ray grating int…
Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Images
Josue Page, Federico Saltarin, Yury Belyaev +2
We present a novel deep learning approach to reconstruct confocal microscopy stacks from single light field images. To perform the reconstruction, we introduce the LFMNet, a novel…
A Gentle Introduction to Deep Learning in Medical Image Processing
Andreas Maier, Christopher Syben, Tobias Lasser +1
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reaso…