What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Radiology
His primary areas of investigation include Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Radiology.
His study connects Bronchoscopy and Artificial intelligence.
His Computer vision study integrates concerns from other disciplines, such as Geodesic and Radiography.
The Segmentation study combines topics in areas such as Weighting, Tomography, Probabilistic logic and Coordinate system.
His research on Pattern recognition also deals with topics like
- Voxel which intersects with area such as Multi organ,
- Focus which is related to area like Salient, Code and Image.
Kensaku Mori works mostly in the field of Radiology, limiting it down to concerns involving Colonoscopy and, occasionally, Clinical trial, Magnification, Optical diagnosis and Medical physics.
His most cited work include:
- Attention U-Net: Learning Where to Look for the Pancreas (581 citations)
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 (242 citations)
- Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation (204 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Radiology.
His Artificial intelligence study focuses mostly on Tracking, Deep learning, Image segmentation, Image and Computer-aided diagnosis.
His Computer vision study incorporates themes from Imaging phantom, Endoscope and Position.
The study incorporates disciplines such as Abdominal ct, Volume, Visualization, Voxel and Multi organ in addition to Segmentation.
His biological study focuses on Convolutional neural network.
His Radiology research includes themes of Nuclear medicine and Lymph node.
He most often published in these fields:
- Artificial intelligence (74.83%)
- Computer vision (45.58%)
- Segmentation (30.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (74.83%)
- Segmentation (30.27%)
- Pattern recognition (25.68%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Deep learning.
His Artificial intelligence study frequently involves adjacent topics like CAD.
His Segmentation study which covers Lung that intersects with Radiology.
His Pattern recognition research incorporates elements of Renal artery, Voronoi diagram, Voxel and Volume.
His work deals with themes such as Endoscope and Virtual reality, which intersect with Computer vision.
His biological study spans a wide range of topics, including Artificial neural network, Polyp size and Sagittal plane.
Between 2018 and 2021, his most popular works were:
- Self-supervised learning for medical image analysis using image context restoration. (64 citations)
- Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). (56 citations)
- Artificial intelligence and colonoscopy: Current status and future perspectives. (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Radiology
- Surgery
Artificial intelligence, Segmentation, Pattern recognition, Convolutional neural network and Colonoscopy are his primary areas of study.
Many of his studies on Artificial intelligence apply to CAD as well.
His research investigates the connection with Segmentation and areas like Field which intersect with concerns in Workflow, Raw data and Generalizability theory.
The various areas that Kensaku Mori examines in his Pattern recognition study include Image, Voxel, Latent variable and Coronal plane.
His Colonoscopy research incorporates themes from Computer aided detection, Radiology and Optical diagnosis.
His biological study deals with issues like Consistency, which deal with fields such as Computer vision.
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