What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Holger R. Roth mainly investigates Artificial intelligence, Convolutional neural network, Computer vision, Segmentation and Pattern recognition.
His study in the fields of Deep learning, Artificial neural network and Contextual image classification under the domain of Artificial intelligence overlaps with other disciplines such as Context.
His research in Deep learning intersects with topics in Minimum bounding box and Medical imaging.
His Contextual image classification research incorporates themes from Transfer of learning, Machine learning and Human body.
His studies deal with areas such as Random forest and Interstitial lung disease as well as Computer vision.
His research in Segmentation is mostly focused on Image segmentation.
His most cited work include:
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning (2315 citations)
- Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation (339 citations)
- DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (284 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Computer vision.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Training set.
When carried out as part of a general Segmentation research project, his work on Image segmentation is frequently linked to work in Field, therefore connecting diverse disciplines of study.
His Pattern recognition research includes themes of Voxel and Feature.
His study in the field of Scale-space segmentation and Tracking is also linked to topics like Supine position.
The study incorporates disciplines such as Contextual image classification, False positive paradox and Computed tomography in addition to Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (78.38%)
- Segmentation (51.35%)
- Pattern recognition (42.16%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (78.38%)
- Image segmentation (21.62%)
- Segmentation (51.35%)
In recent papers he was focusing on the following fields of study:
Holger R. Roth focuses on Artificial intelligence, Image segmentation, Segmentation, Data sharing and Image.
His Artificial intelligence study frequently links to adjacent areas such as Machine learning.
His study in Machine learning is interdisciplinary in nature, drawing from both Annotation and Training set.
His research brings together the fields of Convolutional neural network and Image segmentation.
His work carried out in the field of Segmentation brings together such families of science as Ranking and Data mining.
His Image research integrates issues from Preprocessor and Reinforcement learning.
Between 2020 and 2021, his most popular works were:
- Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. (6 citations)
- Federated learning improves site performance in multicenter deep learning without data sharing. (2 citations)
- UNETR: Transformers for 3D Medical Image Segmentation (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Data sharing, Segmentation, Quality and Convolutional neural network.
Artificial intelligence and Patient data are two areas of study in which Holger R. Roth engages in interdisciplinary research.
His Quality studies intersect with other disciplines such as Information privacy, Machine learning, Annotation, Medical imaging and Domain.
The various areas that Holger R. Roth examines in his Convolutional neural network study include Transformer and Sequence learning.
Along with Locality, other disciplines of study including Image segmentation and Pattern recognition are integrated into his research.
His study brings together the fields of Pooling and Deep learning.
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