What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Computer vision
Milan Sonka mainly investigates Artificial intelligence, Segmentation, Image segmentation, Computer vision and Image processing.
His Artificial intelligence research integrates issues from Tree, Ultrasound and Pattern recognition.
The concepts of his Segmentation study are interwoven with issues in Voxel, Magnetic resonance imaging, Radiology and Airway.
He interconnects Retina, Graph, Edge detection and Active appearance model in the investigation of issues within Image segmentation.
His Computer vision research incorporates elements of Matching, Biplane angiography, Retinal and Medical imaging.
His research on Image processing also deals with topics like
- Region growing most often made with reference to Lung volumes,
- Segmentation-based object categorization together with Binary image, Digital image processing, Feature detection, Automatic image annotation and Directed graph.
His most cited work include:
- Image Processing: Analysis and Machine Vision (3335 citations)
- 3D Slicer as an image computing platform for the Quantitative Imaging Network. (2428 citations)
- Retinal Imaging and Image Analysis (752 citations)
What are the main themes of his work throughout his whole career to date?
Milan Sonka mainly focuses on Artificial intelligence, Segmentation, Computer vision, Image segmentation and Pattern recognition.
While the research belongs to areas of Artificial intelligence, he spends his time largely on the problem of Graph, intersecting his research to questions surrounding Algorithm.
Milan Sonka has included themes like Voxel, Optical coherence tomography, Radiology, Pixel and Biomedical engineering in his Segmentation study.
His study on Optical coherence tomography also encompasses disciplines like
- Retinal that connect with fields like Retina,
- Glaucoma which is related to area like Optic nerve.
He usually deals with Radiology and limits it to topics linked to Coronary arteries and Lumen.
His Computer vision study integrates concerns from other disciplines, such as Principal component analysis and Medical imaging.
He most often published in these fields:
- Artificial intelligence (47.68%)
- Segmentation (38.94%)
- Computer vision (37.05%)
What were the highlights of his more recent work (between 2014-2022)?
- Segmentation (38.94%)
- Artificial intelligence (47.68%)
- Optical coherence tomography (16.12%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Segmentation, Artificial intelligence, Optical coherence tomography, Computer vision and Pattern recognition.
His study on Image segmentation is often connected to Knee mri as part of broader study in Segmentation.
His Artificial intelligence research includes elements of Algorithm and Machine learning, Receiver operating characteristic.
The concepts of his Optical coherence tomography study are interwoven with issues in Cardiac allograft vasculopathy, Biomedical engineering and Confidence interval.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Intravascular ultrasound.
His research integrates issues of Magnetic resonance imaging, Edge detection, Kernel and Calf muscle in his study of Pattern recognition.
Between 2014 and 2022, his most popular works were:
- Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. (218 citations)
- Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. (113 citations)
- Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments (91 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Computer vision
Artificial intelligence, Segmentation, Pattern recognition, Optical coherence tomography and Computer vision are his primary areas of study.
His biological study spans a wide range of topics, including Algorithm and Graph.
His specific area of interest is Segmentation, where Milan Sonka studies Image segmentation.
His Image segmentation research is multidisciplinary, relying on both Radiology and Scintigraphy.
His study in Optical coherence tomography is interdisciplinary in nature, drawing from both Lung transplantation, Biomedical engineering and Confidence interval.
His Computer vision research includes themes of Abdominal ct and Kidney.
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