What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Convolutional neural network, Segmentation and Computer vision.
Artificial intelligence is closely attributed to Pattern recognition in his study.
His Deep learning research is multidisciplinary, incorporating elements of Algorithm and Data set.
His Algorithm research incorporates elements of Contextual image classification, Text mining, Medical diagnosis and Receiver operating characteristic.
In his research on the topic of Convolutional neural network, Artificial neural network is strongly related with Feature extraction.
His Segmentation study combines topics in areas such as Machine learning and Conditional random field.
His most cited work include:
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
- H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (517 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, Deep learning and Machine learning.
The concepts of his Artificial intelligence study are interwoven with issues in Margin and Computer vision.
Qi Dou studied Pattern recognition and Feature that intersect with Normalization.
The concepts of his Segmentation study are interwoven with issues in Leverage and Conditional random field.
Qi Dou integrates many fields in his works, including Deep learning and Breast cancer.
His work in Machine learning addresses issues such as Benchmark, which are connected to fields such as Relation, Medical image computing and Contextual image classification.
He most often published in these fields:
- Artificial intelligence (95.74%)
- Pattern recognition (45.39%)
- Segmentation (37.59%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (95.74%)
- Pattern recognition (45.39%)
- Machine learning (26.95%)
In recent papers he was focusing on the following fields of study:
Qi Dou mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Deep learning.
His biological study spans a wide range of topics, including Margin and Computer vision.
His Pattern recognition research includes themes of Feature and Transformer.
Qi Dou focuses mostly in the field of Machine learning, narrowing it down to matters related to Domain knowledge and, in some cases, Active learning and Dependency.
His Deep learning research is multidisciplinary, incorporating perspectives in Ranking and Medical imaging.
Qi Dou has researched Image in several fields, including Convolution and Convolutional neural network.
Between 2019 and 2021, his most popular works were:
- Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (50 citations)
- Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (41 citations)
- Unpaired Multi-Modal Segmentation via Knowledge Distillation (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary areas of study are Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Feature extraction.
In his work, Task is strongly intertwined with Margin, which is a subfield of Artificial intelligence.
His study in Pattern recognition concentrates on Segmentation and Normalization.
The Deep learning study combines topics in areas such as White matter and Brain segmentation.
Qi Dou interconnects Brain magnetic resonance imaging and Medical imaging in the investigation of issues within Machine learning.
While the research belongs to areas of Feature extraction, Qi Dou spends his time largely on the problem of Discriminative model, intersecting his research to questions surrounding Object detection, Interpolation and Object.
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