What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Hanchuan Peng spends much of his time researching Artificial intelligence, Pattern recognition, Bioimage informatics, Neuroscience and Anatomy.
His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Distance transform.
His work in the fields of Minimum redundancy feature selection overlaps with other areas such as Brain morphometry.
His Minimum redundancy feature selection research includes elements of Phenotype, Microarray analysis techniques, Naive Bayes classifier, Support vector machine and Linear discriminant analysis.
His study in the field of Biological neural network, Nervous system and Neuroanatomical Tract-Tracing Techniques is also linked to topics like Neurite.
The Feature extraction study combines topics in areas such as Data mining, Feature selection and Redundancy.
His most cited work include:
- Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy (6404 citations)
- Minimum redundancy feature selection from microarray gene expression data. (1466 citations)
- A mesoscale connectome of the mouse brain (1338 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Tracing and Neuron are his primary areas of study.
His research on Artificial intelligence often connects related topics like Machine learning.
The Computer vision study which covers Visualization that intersects with Medical imaging and Computer graphics.
His Convolutional neural network and Feature extraction study in the realm of Pattern recognition connects with subjects such as Structure.
His Feature extraction study incorporates themes from Feature selection and Redundancy.
His research investigates the connection between Redundancy and topics such as Data mining that intersect with problems in Cluster analysis.
He most often published in these fields:
- Artificial intelligence (69.84%)
- Computer vision (40.48%)
- Pattern recognition (28.57%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (69.84%)
- Pattern recognition (28.57%)
- Neuron (14.29%)
In recent papers he was focusing on the following fields of study:
Hanchuan Peng mainly investigates Artificial intelligence, Pattern recognition, Neuron, Tracing and Neuroscience.
His research in Artificial intelligence tackles topics such as Computer vision which are related to areas like Morphometric analysis.
His Pattern recognition research is multidisciplinary, relying on both Visualization, Image and Skeleton.
His Visualization study combines topics from a wide range of disciplines, such as Image processing and Datasets as Topic.
Hanchuan Peng interconnects Machine learning, Inhibitory postsynaptic potential, Excitatory postsynaptic potential and Balance in the investigation of issues within Neuron.
He studied Neuroscience and Cell type that intersect with Transcriptome, Visual cortex, Interneuron, GABAergic and Electrophysiology.
Between 2016 and 2021, his most popular works were:
- Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction (51 citations)
- Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells. (35 citations)
- Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Neuroscience, Transcriptome, Cell type and Image segmentation.
Hanchuan Peng combines Artificial intelligence and Tracing in his research.
His studies deal with areas such as Cortex and Function as well as Transcriptome.
Image segmentation is the topic of his studies on Segmentation, Computer vision and Pattern recognition.
He combines subjects such as Parvalbumin, Cortical neurons and Axon with his study of Electrophysiology.
His Iterative reconstruction study incorporates themes from Scale-space segmentation, Segmentation-based object categorization, Deep learning, Voxel and Convolutional neural network.
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