Search Results for author:
Found 11 papers, 3 papers with code
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
Materials and methods: First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLAIR sequences of both time points (CNNdual_all). Second, results were compared with the corresponding single time approaches, in which the CNN was provided exclusively the respective sequences of the diagnosis MRI. Casewise diagnostic performance parameters were calculated from 5-fold cross-validation.
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images.
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
Using this map, the change in tumor volume can be evaluated.
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information.
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs.
Automated brain extraction of multi-sequence MRI using artificial neural networks
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
We propose model-based domain adaptation to overcome input dependencies and avoid re-training of networks at clinical sites by restoring training inputs from altered input channels given during deployment.
Adversarial Networks for Prostate Cancer Detection
The large number of trainable parameters of deep neural networks renders them inherently data hungry.
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies.
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance.
