62 citations · 130 across the 14 of their papers we have counts for
11 papers · 1 filter
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…