What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Mathematical optimization, Computer vision, Algorithm and Image processing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
Hamid R. Tizhoosh interconnects Fuzzy set and Image retrieval in the investigation of issues within Pattern recognition.
The concepts of his Mathematical optimization study are interwoven with issues in Rate of convergence and Benchmark.
His Algorithm research includes themes of Evolutionary algorithm, Evolutionary computation and Ode.
His Image processing study integrates concerns from other disciplines, such as Fuzzy logic, Pixel and Thresholding.
His most cited work include:
- Opposition-Based Differential Evolution (1130 citations)
- Opposition-Based Learning: A New Scheme for Machine Intelligence (799 citations)
- Image thresholding using type II fuzzy sets (245 citations)
What are the main themes of his work throughout his whole career to date?
Hamid R. Tizhoosh mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Image retrieval and Image processing.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Image segmentation, Segmentation, Image, Artificial neural network and Fuzzy logic.
His research in Pattern recognition focuses on subjects like Feature, which are connected to Feature vector.
His work in Computer vision is not limited to one particular discipline; it also encompasses Reinforcement learning.
The various areas that he examines in his Image retrieval study include Local binary patterns and Medical imaging.
His study looks at the relationship between Image processing and topics such as Pixel, which overlap with Histopathology.
He most often published in these fields:
- Artificial intelligence (82.08%)
- Pattern recognition (45.52%)
- Computer vision (30.11%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (82.08%)
- Pattern recognition (45.52%)
- Histopathology (6.81%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Histopathology, Digital pathology and Image retrieval.
His study in Artificial neural network, Deep learning, Image, Magnification and Pixel is carried out as part of his Artificial intelligence studies.
His work in Pattern recognition addresses subjects such as Radon transform, which are connected to disciplines such as Medical imaging.
His Digital pathology study incorporates themes from Segmentation and Cancer genome.
His Image retrieval research is multidisciplinary, relying on both Somatic cell, Cancer gene and Grayscale.
Hamid R. Tizhoosh interconnects Computer vision and Microscopy in the investigation of issues within Microscope.
Between 2018 and 2021, his most popular works were:
- Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence (20 citations)
- Yottixel - An Image Search Engine for Large Archives of Histopathology Whole Slide Images. (13 citations)
- Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Pattern recognition, Digital pathology, Histopathology and Deep learning.
All of his Artificial intelligence and Artificial neural network, Image retrieval, Image, Pixel and Feature learning investigations are sub-components of the entire Artificial intelligence study.
Hamid R. Tizhoosh combines subjects such as Lung cancer, Magnification and Adenocarcinoma with his study of Pattern recognition.
In his research, Search engine, Information retrieval and Computational pathology is intimately related to Cancer genome, which falls under the overarching field of Digital pathology.
His Histopathology research is multidisciplinary, incorporating perspectives in Pan cancer, Segmentation, H&E stain and Medical diagnosis.
The concepts of his Deep learning study are interwoven with issues in Contextual image classification, Generative grammar, Generative model and Diagnostic quality.
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