What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Lin Yang mostly deals with Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Convolutional neural network.
Image, Deep learning, Mean-shift, Discriminative model and Image resolution are among the areas of Artificial intelligence where she concentrates her study.
Lin Yang combines subjects such as 3D ultrasound, Scalability and Microscopy with her study of Computer vision.
Her study on Image segmentation is often connected to Throughput as part of broader study in Segmentation.
Her research integrates issues of Object and Active appearance model in her study of Pattern recognition.
Her work deals with themes such as Classifier, Image registration and Medical imaging, which intersect with Convolutional neural network.
Her most cited work include:
- Robust tracking using local sparse appearance model and K-selection (437 citations)
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (257 citations)
- Inducible depletion of satellite cells in adult, sedentary mice impairs muscle regenerative capacity without affecting sarcopenia. (242 citations)
What are the main themes of her work throughout her whole career to date?
Lin Yang focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Convolutional neural network.
Her is doing research in Image segmentation, Deep learning, Image, Pixel and Histogram, both of which are found in Artificial intelligence.
Her studies deal with areas such as Contextual image classification, Feature and Image retrieval as well as Pattern recognition.
Her Segmentation research is multidisciplinary, incorporating elements of Recurrent neural network and Training set.
Her work in Computer vision addresses issues such as Medical imaging, which are connected to fields such as Cancer.
She has researched Convolutional neural network in several fields, including Lesion and Magnetic resonance imaging.
She most often published in these fields:
- Artificial intelligence (77.05%)
- Pattern recognition (48.09%)
- Segmentation (32.79%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (77.05%)
- Pattern recognition (48.09%)
- Convolutional neural network (21.86%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Artificial intelligence, Pattern recognition, Convolutional neural network, Segmentation and Deep learning.
Her research brings together the fields of Machine learning and Artificial intelligence.
Her Pattern recognition study integrates concerns from other disciplines, such as Lesion and Feature.
Her Convolutional neural network research incorporates themes from Leverage, Semantic information, Pixel, Calibration and Magnetic resonance imaging.
She studies Segmentation, namely Image segmentation.
Her Deep learning research includes elements of Image processing, Cancer and Computer vision.
Between 2017 and 2021, her most popular works were:
- Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network (184 citations)
- Deep Learning in Microscopy Image Analysis: A Survey (127 citations)
- Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network (122 citations)
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