Author
Ursula Schmidt-Erfurth
Other affiliations: University of Vienna, Yahoo!, Massachusetts Eye and Ear Infirmary ...read more
Bio: Ursula Schmidt-Erfurth is an academic researcher from Medical University of Vienna. The author has contributed to research in topics: Macular degeneration & Optical coherence tomography. The author has an hindex of 82, co-authored 638 publications receiving 28143 citations. Previous affiliations of Ursula Schmidt-Erfurth include University of Vienna & Yahoo!.
Papers published on a yearly basis
Papers
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TL;DR: It is demonstrated that aflibercept is an effective treatment for AMD, with the every-2-month regimen offering the potential to reduce the risk from monthly intravitreal injections and the burden of monthly monitoring.
1,894 citations
25 Jun 2017
TL;DR: AnoGAN, a deep convolutional generative adversarial network is proposed to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space.
Abstract: Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.
1,800 citations
TL;DR: Ranibizumab monotherapy and combined with laser provided superior visual acuity gain over standard laser in patients with visual impairment due to DME and had a safety profile in DME similar to that in age-related macular degeneration.
1,187 citations
TL;DR: Fast AnoGAN (f‐AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates is presented.
777 citations
TL;DR: All aflibercept and ranibizumab groups were equally effective in improving BCVA and preventing BCVA loss at 96 weeks, and the 2q8 a flibercept group was similar to ranibIZumab in visual acuity outcomes during 96 weeks, but with an average of 5 fewer injections.
688 citations
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Journal Article•
9,185 citations
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
8,730 citations
TL;DR: PDT is being tested in the clinic for use in oncology — to treat cancers of the head and neck, brain, lung, pancreas, intraperitoneal cavity, breast, prostate and skin.
Abstract: The therapeutic properties of light have been known for thousands of years, but it was only in the last century that photodynamic therapy (PDT) was developed. At present, PDT is being tested in the clinic for use in oncology--to treat cancers of the head and neck, brain, lung, pancreas, intraperitoneal cavity, breast, prostate and skin. How does PDT work, and how can it be used to treat cancer and other diseases?
5,041 citations
Journal Article•
3,940 citations
TL;DR: The second iteration of the European Society of Cardiology (ESC) and European Association for the Study of Diabetes (EASD) joining forces to write guidelines on the management of diabetes mellitus (DM), pre-diabetes, and cardiovascular disease (CVD), designed to assist clinicians and other healthcare workers to make evidence-based management decisions.
Abstract: This is the second iteration of the European Society of Cardiology (ESC) and European Association for the Study of Diabetes (EASD) joining forces to write guidelines on the management of diabetes mellitus (DM), pre-diabetes, and cardiovascular disease (CVD), designed to assist clinicians and other healthcare workers to make evidence-based management decisions. The growing awareness of the strong biological relationship between DM and CVD rightly prompted these two large organizations to collaborate to generate guidelines relevant to their joint interests, the first of which were published in 2007. Some assert that too many guidelines are being produced but, in this burgeoning field, five years in the development of both basic and clinical science is a long time and major trials have reported in this period, making it necessary to update the previous Guidelines.
2,809 citations