What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Surgery
- Radiology
His primary areas of study are Artificial intelligence, Segmentation, Computer vision, Image segmentation and Pattern recognition.
His Artificial intelligence research incorporates elements of Machine learning and Regression.
His studies deal with areas such as Algorithm, Bhattacharyya distance, Mathematical optimization and Similarity as well as Segmentation.
His Mathematical optimization research integrates issues from Ordinal regression, Finite set and Benchmark.
His research integrates issues of Prior probability, Intervertebral disc and Hausdorff distance in his study of Computer vision.
His work on Softmax function as part of general Pattern recognition research is often related to Estimation, thus linking different fields of science.
His most cited work include:
- Incremental Support Vector Learning for Ordinal Regression (611 citations)
- Incremental learning for ν -Support Vector Regression (345 citations)
- Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. (199 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Image segmentation are his primary areas of study.
His Deep learning, Feature, Support vector machine, Pixel and Feature extraction investigations are all subjects of Artificial intelligence research.
His work is dedicated to discovering how Segmentation, Bhattacharyya distance are connected with Mathematical optimization and other disciplines.
His research in Pattern recognition focuses on subjects like Regression, which are connected to Machine learning.
His Computer vision research is multidisciplinary, relying on both Classifier, Boundary and Prior probability.
His research on Image segmentation frequently links to adjacent areas such as Medical imaging.
He most often published in these fields:
- Artificial intelligence (65.03%)
- Segmentation (38.65%)
- Pattern recognition (36.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.03%)
- Pattern recognition (36.81%)
- Segmentation (38.65%)
In recent papers he was focusing on the following fields of study:
Shuo Li mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Feature.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His work carried out in the field of Pattern recognition brings together such families of science as Ground truth, Sequence, Reinforcement learning and Vertebra.
Shuo Li combines subjects such as Anatomy, Joint, Regression and Medical imaging with his study of Segmentation.
Shuo Li has researched Deep learning in several fields, including Object detection, Inference, Residual and Left atrium.
His work deals with themes such as Context, Contrast, Frame, Image and Convolution, which intersect with Feature.
Between 2019 and 2021, his most popular works were:
- Salient Object Detection in the Distributed Cloud-Edge Intelligent Network (23 citations)
- IoT-based 3D convolution for video salient object detection (22 citations)
- Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Surgery
- Internal medicine
Shuo Li mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Feature and Image segmentation.
His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision.
His studies in Pattern recognition integrate themes in fields like Correlation coefficient, Coronary stenosis, Regression, Magnetic resonance imaging and Vertebra.
Shuo Li has included themes like Lesion, Pixel, Anatomy, Spinal disease and Intervertebral foramen in his Segmentation study.
His biological study spans a wide range of topics, including Semi-supervised learning, Feature extraction, Angiography and Contrast.
His Image segmentation research focuses on Medical imaging and how it connects with Projection, Image and Dimensionality reduction.
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