What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
His primary areas of study are Artificial intelligence, Computer vision, Noise reduction, Pattern recognition and Non-local means.
His research on Artificial intelligence focuses in particular on Segmentation.
His Segmentation research includes elements of Image processing and Neuroimaging.
His Noise reduction research is multidisciplinary, relying on both Filter, Wavelet, Noise measurement, Principal component analysis and Gaussian noise.
His Pattern recognition research integrates issues from Magnetic resonance imaging and Robustness.
The study incorporates disciplines such as Image scaling and Peak signal-to-noise ratio in addition to Non-local means.
His most cited work include:
- Adaptive non‐local means denoising of MR images with spatially varying noise levels (587 citations)
- Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. (554 citations)
- MRI denoising using non-local means. (370 citations)
What are the main themes of his work throughout his whole career to date?
José V. Manjón mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Noise reduction.
Deep learning, Non-local means, Brain segmentation, Image processing and Robustness are the subjects of his Artificial intelligence studies.
His research integrates issues of Grading, Image and Early detection in his study of Pattern recognition.
His work on Scale-space segmentation as part of general Segmentation study is frequently connected to Fusion, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Many of his research projects under Computer vision are closely connected to Self-similarity with Self-similarity, tying the diverse disciplines of science together.
His research investigates the connection between Noise reduction and topics such as Filter that intersect with problems in Image noise.
He most often published in these fields:
- Artificial intelligence (66.07%)
- Pattern recognition (41.96%)
- Segmentation (36.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (66.07%)
- Pattern recognition (41.96%)
- Deep learning (11.61%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network.
José V. Manjón is studying Image processing, which is a component of Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Hippocampus and Cognition, Cognitive impairment.
His Segmentation study combines topics from a wide range of disciplines, such as Intracranial Cavity and Multi feature.
His work deals with themes such as Normalization and Noise reduction, which intersect with Convolutional neural network.
As a part of the same scientific family, José V. Manjón mostly works in the field of Brain segmentation, focusing on Robustness and, on occasion, Brain mri and Computer vision.
Between 2018 and 2021, his most popular works were:
- Lifespan Changes of the Human Brain In Alzheimer’s Disease (31 citations)
- Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. (19 citations)
- Differential annualized rates of hippocampal subfields atrophy in aging and future Alzheimer's clinical syndrome (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Deep learning, Artificial intelligence, Convolutional neural network, Pattern recognition and Hippocampal formation.
His research in Deep learning intersects with topics in Segmentation and Brain segmentation.
His studies in Brain segmentation integrate themes in fields like Transfer of learning, Ensemble learning and Machine learning.
His Pattern recognition research includes themes of Brain mri, Noise reduction and Robustness.
His Hippocampal formation research is multidisciplinary, incorporating perspectives in Clinical syndrome, Disease, Pediatrics and Atrophy.
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