What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Computer vision
His main research concerns Artificial intelligence, Image segmentation, Voxel, Image processing and Materials science.
His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
In most of his Image segmentation studies, his work intersects topics such as Fuzzy logic.
His Voxel research includes themes of Magnetic resonance imaging, Skeletonization, Partial volume, Bone remodeling and Topology.
Punam K. Saha has included themes like Ankle, Subtalar joint and 3d positioning in his Image processing study.
His Materials science research incorporates Trabecular bone, Biomedical engineering and Osteoporosis.
His most cited work include:
- Scale-Based Fuzzy Connected Image Segmentation (279 citations)
- Digital Topological Analysis of In Vivo Magnetic Resonance Microimages of Trabecular Bone Reveals Structural Implications of Osteoporosis (202 citations)
- Detection of 3-D simple points for topology preserving transformations with application to thinning (182 citations)
What are the main themes of his work throughout his whole career to date?
Punam K. Saha mostly deals with Artificial intelligence, Computer vision, Segmentation, Image segmentation and Osteoporosis.
His biological study deals with issues like Pattern recognition, which deal with fields such as Deep learning.
His Computer vision study deals with Intensity intersecting with Polynomial.
His Segmentation research incorporates themes from Algorithm and Magnetic resonance imaging.
Punam K. Saha has researched Image segmentation in several fields, including Object, Histogram, Pixel and Thresholding.
In general Osteoporosis study, his work on Bone mineral and Trabecular bone often relates to the realm of Materials science, thereby connecting several areas of interest.
He most often published in these fields:
- Artificial intelligence (42.68%)
- Computer vision (31.38%)
- Segmentation (26.78%)
What were the highlights of his more recent work (between 2017-2021)?
- Segmentation (26.78%)
- Artificial intelligence (42.68%)
- Osteoporosis (23.01%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Segmentation, Artificial intelligence, Osteoporosis, Nuclear medicine and Trabecular bone.
His Segmentation study incorporates themes from Dendritic spine and Algorithm.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
His work on FRAX as part of general Osteoporosis study is frequently connected to Materials science, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Nuclear medicine research integrates issues from Bone development, Bone mineral, Reproducibility and Ct scanners, Computed tomography.
His Trabecular bone research incorporates elements of Cortical bone, Reduced bone density and Cadaveric spasm.
Between 2017 and 2021, his most popular works were:
- CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (94 citations)
- CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE) (49 citations)
- Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Computer vision
Artificial intelligence, Osteoporosis, Image resolution, Pattern recognition and Fuzzy logic are his primary areas of study.
As part of one scientific family, he deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Algorithm, and often Linear discriminant analysis.
The concepts of his Osteoporosis study are interwoven with issues in Nuclear medicine and Receiver operating characteristic.
In Nuclear medicine, Punam K. Saha works on issues like Correlation, which are connected to Trabecular bone.
His Pattern recognition study frequently intersects with other fields, such as Voxel.
His studies in Fuzzy logic integrate themes in fields like Segmentation, Computer vision and Distance transform.
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