What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- Enzyme
Li Wang focuses on Artificial intelligence, Segmentation, Magnetic resonance imaging, Neuroscience and Gene.
His Artificial intelligence research incorporates themes from White matter, Computer vision and Pattern recognition.
His Computer vision study combines topics from a wide range of disciplines, such as Partial volume, Sparse approximation and Brain tissue.
His research related to Image segmentation and Level set method might be considered part of Segmentation.
His work on Active contour model as part of general Image segmentation research is often related to Initialization, thus linking different fields of science.
His research in Deep learning intersects with topics in Image and Convolutional neural network.
His most cited work include:
- An integrated encyclopedia of DNA elements in the human genome (2642 citations)
- Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. (491 citations)
- Photocatalytic Activity Enhanced via g-C3N4 Nanoplates to Nanorods (457 citations)
What are the main themes of his work throughout his whole career to date?
Li Wang focuses on Internal medicine, Artificial intelligence, Chemical engineering, Computer network and Pattern recognition.
Li Wang combines subjects such as Gastroenterology, Endocrinology and Oncology with his study of Internal medicine.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Magnetic resonance imaging and Computer vision.
His Computer network study typically links adjacent topics like Wireless.
He most often published in these fields:
- Internal medicine (11.71%)
- Artificial intelligence (10.63%)
- Chemical engineering (6.80%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (10.63%)
- Internal medicine (11.71%)
- Chemical engineering (6.80%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Internal medicine, Chemical engineering, Cancer research and Pattern recognition.
The various areas that he examines in his Artificial intelligence study include Machine learning and Computer vision.
The Internal medicine study combines topics in areas such as Endocrinology and Oncology.
His Chemical engineering study integrates concerns from other disciplines, such as Electrolyte and Electrochemistry.
His Electrolyte research integrates issues from Battery and Anode.
His Cancer research study often links to related topics such as Lymphoma.
Between 2019 and 2021, his most popular works were:
- Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation (30 citations)
- Nanoparticle enhanced combination therapy for stem-like progenitors defined by single-cell transcriptomics in chemotherapy-resistant osteosarcoma (23 citations)
- Antibiotics in soil and water in China-a systematic review and source analysis. (18 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- Enzyme
His primary areas of study are Artificial intelligence, Cancer research, Internal medicine, Nanotechnology and Chemical engineering.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His Internal medicine research is multidisciplinary, incorporating elements of Genetic model, Endocrinology and Oncology.
His Nanotechnology research is multidisciplinary, relying on both Orange juice and Raman spectroscopy.
In his research, Ion, Electrolyte and Lithium is intimately related to Electrochemistry, which falls under the overarching field of Chemical engineering.
His Segmentation study improves the overall literature in Computer vision.
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