What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Surgery
His main research concerns Artificial intelligence, Computer vision, Augmented reality, Segmentation and Pose.
His research brings together the fields of Pattern recognition and Artificial intelligence.
His Computer vision research includes themes of Computer graphics and Ultrasound.
His Augmented reality study also includes fields such as
- Visualization which is related to area like Illusion,
- Multimedia which connect with Mixed reality.
He has researched Segmentation in several fields, including Labeled data and Benchmark.
His research investigates the connection with Pose and areas like Video tracking which intersect with concerns in Tracking system.
His most cited work include:
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2365 citations)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
- Deeper Depth Prediction with Fully Convolutional Residual Networks (981 citations)
What are the main themes of his work throughout his whole career to date?
Nassir Navab mainly focuses on Artificial intelligence, Computer vision, Augmented reality, Pattern recognition and Segmentation.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His studies deal with areas such as Imaging phantom, Ultrasound, Computer graphics and Robustness as well as Computer vision.
His Augmented reality study necessitates a more in-depth grasp of Human–computer interaction.
His research is interdisciplinary, bridging the disciplines of Feature and Pattern recognition.
Specifically, his work in Segmentation is concerned with the study of Image segmentation.
He most often published in these fields:
- Artificial intelligence (82.98%)
- Computer vision (57.52%)
- Augmented reality (19.98%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (82.98%)
- Computer vision (57.52%)
- Machine learning (9.74%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Segmentation and Pattern recognition.
His study in Deep learning, Convolutional neural network, Artificial neural network, Augmented reality and Medical imaging is carried out as part of his studies in Artificial intelligence.
His Computer vision research includes elements of Imaging phantom and Visualization.
In his work, Graph is strongly intertwined with Graph, which is a subfield of Machine learning.
A large part of his Segmentation studies is devoted to Image segmentation.
Nassir Navab has included themes like Ground truth and Robustness in his Pattern recognition study.
Between 2018 and 2021, his most popular works were:
- Staingan: Stain Style Transfer for Digital Histological Images (83 citations)
- QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. (83 citations)
- QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. (83 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Surgery
Nassir Navab focuses on Artificial intelligence, Computer vision, Deep learning, Segmentation and Pattern recognition.
He combines subjects such as Machine learning and Graph with his study of Artificial intelligence.
The various areas that he examines in his Computer vision study include Visualization and Benchmark.
The concepts of his Deep learning study are interwoven with issues in Embedding and Data mining.
His biological study spans a wide range of topics, including Pixel and Medical imaging.
In general Pattern recognition, his work in Training set is often linked to Transfer linking many areas of study.
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