What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Computer vision, Convolutional neural network, Segmentation and Deep learning.
His biological study spans a wide range of topics, including Machine learning, Breast cancer, Residual and Pattern recognition.
His research integrates issues of Margin and Standard plane in his study of Computer vision.
His studies in Convolutional neural network integrate themes in fields like Feature extraction and Training set.
His work on Image segmentation and Scale-space segmentation as part of general Segmentation research is frequently linked to Intestinal gland, thereby connecting diverse disciplines of science.
His Deep learning research incorporates themes from Algorithm, Reduction and Data set.
His most cited work include:
- 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)
- Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (409 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, Deep learning, Segmentation and Convolutional neural network.
His Artificial intelligence research includes themes of Margin, Machine learning and Computer vision.
His research investigates the link between Pattern recognition and topics such as Robustness that cross with problems in Backpropagation.
His work in Deep learning addresses issues such as Breast cancer, which are connected to fields such as Lymph node and Histology.
His study on Image segmentation and Scale-space segmentation is often connected to Encoder as part of broader study in Segmentation.
His Convolutional neural network study integrates concerns from other disciplines, such as Pixel and Recurrent neural network.
He most often published in these fields:
- Artificial intelligence (91.30%)
- Pattern recognition (51.45%)
- Deep learning (45.65%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (91.30%)
- Deep learning (45.65%)
- Pattern recognition (51.45%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Machine learning are his primary areas of study.
His research in Artificial intelligence focuses on subjects like Margin, which are connected to Task.
He interconnects Optical coherence tomography, Digital pathology, Medical imaging, Robustness and Receiver operating characteristic in the investigation of issues within Deep learning.
His work on Convolutional neural network as part of general Pattern recognition research is frequently linked to Memory bank and Metric, bridging the gap between disciplines.
The Convolutional neural network study combines topics in areas such as Pixel and Pulmonary nodule.
He has included themes like Annotation, Cancer and Lung cancer in his Machine learning study.
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)
- A Multi-Organ Nucleus Segmentation Challenge (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Cancer
Hao Chen mainly focuses on Artificial intelligence, Deep learning, Pattern recognition, Margin and Machine learning.
Hao Chen integrates several fields in his works, including Artificial intelligence and Multi-task learning.
His Deep learning study frequently links to related topics such as Image.
The concepts of his Machine learning study are interwoven with issues in Cancer and Lung cancer.
His studies deal with areas such as Random forest and Feature extraction as well as Cancer.
Image segmentation is the subject of his research, which falls under Segmentation.
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