What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Retina
Meindert Niemeijer spends much of his time researching Fundus, Artificial intelligence, Diabetic retinopathy, Computer vision and Retinopathy.
Meindert Niemeijer combines subjects such as Area under the curve and Macular edema with his study of Artificial intelligence.
In his study, which falls under the umbrella issue of Diabetic retinopathy, False positive paradox, Optometry and Deep learning is strongly linked to Surgery.
His work in Computer vision addresses subjects such as Retina, which are connected to disciplines such as Pixel and Image segmentation.
Meindert Niemeijer interconnects Computer-aided diagnosis and Receiver operating characteristic in the investigation of issues within Retinopathy.
His research in Receiver operating characteristic intersects with topics in Ophthalmology, Pathology, Cotton wool spots, Drusen and Fundus photography.
His most cited work include:
- Ridge-based vessel segmentation in color images of the retina (2322 citations)
- Comparative study of retinal vessel segmentation methods on a new publicly available database (651 citations)
- Comparative study of retinal vessel segmentation methods on a new publicly available database (651 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Retinal, Fundus and Diabetic retinopathy.
Meindert Niemeijer has researched Artificial intelligence in several fields, including Retina and Pattern recognition.
His Computer vision research integrates issues from Optical coherence tomography and Optic disc.
Retinal is a subfield of Ophthalmology that Meindert Niemeijer studies.
His Diabetic retinopathy research is multidisciplinary, incorporating elements of Surgery, Retinopathy, Optometry, Pathology and Receiver operating characteristic.
As a part of the same scientific study, Meindert Niemeijer usually deals with the Segmentation, concentrating on Image database and frequently concerns with Manual segmentation and Database.
He most often published in these fields:
- Artificial intelligence (64.36%)
- Computer vision (55.45%)
- Retinal (41.58%)
What were the highlights of his more recent work (between 2013-2018)?
- Artificial intelligence (64.36%)
- Retinal (41.58%)
- Computer vision (55.45%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Retinal, Computer vision, Image and Diabetic retinopathy.
His Artificial intelligence research includes themes of Retinal image, Fundus and Optic disk.
His Fundus research is multidisciplinary, incorporating perspectives in Macular edema, Retrospective cohort study, Confidence interval, Tree and Translation.
His work deals with themes such as Pixel and Detector, which intersect with Retinal.
His study in the fields of Image processing, Image segmentation, Segmentation and Feature extraction under the domain of Computer vision overlaps with other disciplines such as Stationary wavelet transform.
His study looks at the relationship between Diabetic retinopathy and topics such as Deep learning, which overlap with Surgery, Area under the curve, Internal medicine and Convolutional neural network.
Between 2013 and 2018, his most popular works were:
- Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. (349 citations)
- End-to-End Adversarial Retinal Image Synthesis (163 citations)
- Towards Adversarial Retinal Image Synthesis (81 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Retina
Artificial intelligence, Fundus, Retinal, Image and Computer vision are his primary areas of study.
In general Artificial intelligence, his work in Translation and Deep learning is often linked to Generative model linking many areas of study.
His research integrates issues of Tree and Retinal image in his study of Translation.
The concepts of his Deep learning study are interwoven with issues in Internal medicine, Retrospective cohort study, Confidence interval and Macular edema.
Generative model is connected with Image processing, Artificial neural network, End-to-end principle, Training set and Autoencoder in his research.
His study on Reference standards is intertwined with other disciplines of science such as Surgery, Area under the curve and Diabetic retinopathy.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
