What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Computer vision, Deep learning, Segmentation and Pattern recognition.
His Artificial intelligence study frequently draws connections to other fields, such as Ultrasound.
His work on Feature and Image processing is typically connected to Process as part of general Computer vision study, connecting several disciplines of science.
His work on Scale-space segmentation as part of general Segmentation study is frequently linked to Imaging quality, bridging the gap between disciplines.
His Pattern recognition research is multidisciplinary, relying on both Machine learning and Neuroimaging.
Dong Ni studied Feature extraction and Artificial neural network that intersect with Medical image computing.
His most cited work include:
- Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (345 citations)
- Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks (198 citations)
- Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning (165 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Ultrasound.
In most of his Artificial intelligence studies, his work intersects topics such as Machine learning.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network and Image, Fisher vector.
In his study, 3D ultrasound and Biometrics is inextricably linked to Image quality, which falls within the broad field of Segmentation.
When carried out as part of a general Computer vision research project, his work on Feature, Scale-space segmentation and Image registration is frequently linked to work in Standard plane and Point set registration, therefore connecting diverse disciplines of study.
The Ultrasound study combines topics in areas such as Imaging phantom, Fetal head and Biomedical engineering.
He most often published in these fields:
- Artificial intelligence (74.37%)
- Pattern recognition (40.70%)
- Segmentation (29.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (74.37%)
- Pattern recognition (40.70%)
- Segmentation (29.15%)
In recent papers he was focusing on the following fields of study:
Dong Ni focuses on Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Image segmentation.
He has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His biological study spans a wide range of topics, including 3D ultrasound, Image, Consistency and Reinforcement learning.
The various areas that he examines in his Segmentation study include Image quality, Convolutional neural network and Biometrics.
His study explores the link between Deep learning and topics such as Landmark that cross with problems in Minimum bounding box, Object detection and Artificial neural network.
His Ultrasound image segmentation study in the realm of Image segmentation connects with subjects such as Adaptation and Invariant.
Between 2019 and 2021, his most popular works were:
- Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound (17 citations)
- CR-Unet: A Composite Network for Ovary and Follicle Segmentation in Ultrasound Images (10 citations)
- A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Segmentation, Medical imaging, Pattern recognition and Convolutional neural network.
His Artificial intelligence study incorporates themes from Machine learning, Functional connectivity and Identification.
He is interested in Image segmentation, which is a field of Segmentation.
His Image segmentation research is classified as research in Computer vision.
As part of the same scientific family, Dong Ni usually focuses on Medical imaging, concentrating on Discriminative model and intersecting with Feature, Skin lesion, Object and Melanoma.
As a member of one scientific family, Dong Ni mostly works in the field of Pattern recognition, focusing on Deep learning and, on occasion, Kernel and Ultrasonic imaging.
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