What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Weidong Cai mainly investigates Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Contextual image classification.
His research ties Machine learning and Artificial intelligence together.
His studies examine the connections between Pattern recognition and genetics, as well as such issues in Feature, with regards to Feature detection.
His research integrates issues of Image processing, Image texture, Binary classification, Support vector machine and Voxel in his study of Feature extraction.
In his study, Kernel, Human–computer information retrieval and Information retrieval is strongly linked to Medical imaging, which falls under the umbrella field of Computer vision.
Weidong Cai interconnects Neuroimaging, Cancer, Feature learning and Algorithm in the investigation of issues within Deep learning.
His most cited work include:
- Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease (256 citations)
- Medical image classification with convolutional neural network (235 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?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Feature extraction.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His research on Pattern recognition frequently links to adjacent areas such as Image.
His Computer vision research is multidisciplinary, incorporating elements of Positron emission tomography and Medical imaging.
His Segmentation research includes elements of Image resolution and Thresholding.
His Feature extraction research integrates issues from Neuroimaging, Image retrieval and Image texture.
He most often published in these fields:
- Artificial intelligence (80.00%)
- Pattern recognition (50.18%)
- Computer vision (32.00%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (80.00%)
- Pattern recognition (50.18%)
- Segmentation (21.09%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Convolutional neural network.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.
His Pattern recognition research incorporates themes from Contextual image classification and Kernel.
He combines subjects such as Image resolution, Inpainting and Digital pathology with his study of Segmentation.
Weidong Cai focuses mostly in the field of Deep learning, narrowing it down to matters related to Task and, in some cases, Computer engineering.
Weidong Cai has included themes like Cancer and Image retrieval in his Feature extraction study.
Between 2017 and 2021, his most popular works were:
- Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (88 citations)
- Multiscale Network Followed Network Model for Retinal Vessel Segmentation (73 citations)
- Reversion Correction and Regularized Random Walk Ranking for Saliency Detection. (54 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, Pattern recognition, Segmentation, Deep learning and Feature.
His work on Image segmentation as part of his general Artificial intelligence study is frequently connected to Diabetic retinopathy, thereby bridging the divide between different branches of science.
His specific area of interest is Pattern recognition, where Weidong Cai studies Feature extraction.
The study incorporates disciplines such as Soma and Digital pathology in addition to Segmentation.
Weidong Cai has researched Deep learning in several fields, including Pyramid, Encoding, Fundus, Convolutional neural network and Pyramid.
His Tractography study incorporates themes from Image processing, Machine learning and Neuroscience.
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