What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Radiology
Bram van Ginneken mainly investigates Artificial intelligence, Segmentation, Radiology, Computer vision and Computed tomography.
Bram van Ginneken regularly ties together related areas like Machine learning in his Artificial intelligence studies.
His Segmentation study integrates concerns from other disciplines, such as Pixel and Active appearance model.
His work carried out in the field of Radiology brings together such families of science as Lung and Receiver operating characteristic.
Bram van Ginneken interconnects False positive paradox, Set, Support vector machine and Database in the investigation of issues within Computer vision.
His studies in Computed tomography integrate themes in fields like Algorithm, Malignancy and Data set.
His most cited work include:
- A survey on deep learning in medical image analysis (4326 citations)
- Reflectance and texture of real-world surfaces (1079 citations)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision.
His research links Computed tomography with Artificial intelligence.
His Radiology study combines topics in areas such as Lung cancer, Lung and Nuclear medicine.
His Segmentation research includes themes of Artificial neural network, Tomography and Image processing, Image.
He has included themes like Contextual image classification, Image registration and Radiography in his Pattern recognition study.
His Computer vision course of study focuses on Retina and Fundus.
He most often published in these fields:
- Artificial intelligence (50.36%)
- Radiology (32.78%)
- Segmentation (28.03%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (50.36%)
- Pattern recognition (23.99%)
- Segmentation (28.03%)
In recent papers he was focusing on the following fields of study:
Bram van Ginneken focuses on Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Radiology.
He works mostly in the field of Artificial intelligence, limiting it down to concerns involving Computer vision and, occasionally, Field.
His work on Training set is typically connected to Metric as part of general Pattern recognition study, connecting several disciplines of science.
His Segmentation research is multidisciplinary, relying on both Algorithm and Image, Similarity.
His study focuses on the intersection of Deep learning and fields such as Inpainting with connections in the field of Encoder.
His research in Radiology intersects with topics in Lung cancer, Lung and Receiver operating characteristic.
Between 2017 and 2021, his most popular works were:
- CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation. (216 citations)
- A large annotated medical image dataset for the development and evaluation of segmentation algorithms (203 citations)
- The Liver Tumor Segmentation Benchmark (LiTS) (133 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Radiology
Bram van Ginneken mostly deals with Radiology, Artificial intelligence, Segmentation, Lung cancer and Receiver operating characteristic.
Radiology connects with themes related to Lung in his study.
His Deep learning and Convolutional neural network study in the realm of Artificial intelligence connects with subjects such as Kappa.
Bram van Ginneken interconnects Interactive visualization, Field, Image and Labeled data in the investigation of issues within Segmentation.
His research integrates issues of Calcification, Thoracic aorta, Cohort and Computed tomography in his study of Lung cancer.
His Receiver operating characteristic research is multidisciplinary, incorporating elements of Radiological weapon, Stroma, Radiography and Confidence interval.
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