What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Image segmentation, Positron emission tomography and Segmentation.
His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Image processing, Image and Feature.
His Image segmentation research incorporates themes from Image resolution, Imaging phantom and Cluster analysis.
Dagan Feng interconnects Algorithm and Non-linear least squares in the investigation of issues within Positron emission tomography.
His Feature extraction research incorporates elements of Voxel and Image retrieval.
His most cited work include:
- Accelerating magnetic resonance imaging via deep learning (376 citations)
- Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease (256 citations)
- Early diagnosis of Alzheimer's disease with deep learning (201 citations)
What are the main themes of his work throughout his whole career to date?
Dagan Feng mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Positron emission tomography.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Artificial neural network and Feature.
His Computer vision research is multidisciplinary, incorporating perspectives in Visualization and Medical imaging.
In his research on the topic of Segmentation, Modality is strongly related with PET-CT.
Dagan Feng interconnects Estimation theory and Algorithm in the investigation of issues within Positron emission tomography.
He most often published in these fields:
- Artificial intelligence (68.94%)
- Pattern recognition (42.66%)
- Computer vision (33.11%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (68.94%)
- Pattern recognition (42.66%)
- Deep learning (9.90%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Computer vision are his primary areas of study.
His research brings together the fields of Machine learning and Artificial intelligence.
He has researched Pattern recognition in several fields, including Contextual image classification and Feature.
Dagan Feng has included themes like Modality and Kernel in his Deep learning study.
His work on Image segmentation is typically connected to Block as part of general Segmentation study, connecting several disciplines of science.
His Computer vision study incorporates themes from Point and Optical coherence tomography.
Between 2015 and 2021, his most popular works were:
- Accelerating magnetic resonance imaging via deep learning (376 citations)
- Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (88 citations)
- Transfer learning of a convolutional neural network for HEp-2 cell image classification (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Dagan Feng spends much of his time researching Artificial intelligence, Pattern recognition, Convolutional neural network, Segmentation and Deep learning.
Artificial intelligence and Computer vision are frequently intertwined in his study.
His Computer vision research includes elements of Identification, Magnetic resonance imaging, Mr images and Ultrasound.
His Pattern recognition research incorporates themes from Contextual image classification, Image and Representation.
His biological study spans a wide range of topics, including Lung tumor, PET-CT, Surgical planning and Constraint.
Dagan Feng works mostly in the field of Deep learning, limiting it down to concerns involving Feature learning and, occasionally, Contrast, Linear combination, Sparse approximation and Neutral network.
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