What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Nasir M. Rajpoot focuses on Artificial intelligence, Digital pathology, Pattern recognition, Deep learning and Segmentation.
His Artificial intelligence research incorporates elements of Machine learning and Computer vision.
His Digital pathology study combines topics in areas such as Deconvolution, Algorithm, Medical imaging, Breast cancer and Histopathology.
His Deep learning research incorporates themes from Tumor heterogeneity, Divergence, Convolutional neural network and Test set.
His work in Test set tackles topics such as Text mining which are related to areas like Contextual image classification.
Nasir M. Rajpoot works mostly in the field of Segmentation, limiting it down to concerns involving Histology and, occasionally, Colorectal cancer, Colorectal adenocarcinoma and Bayesian inference.
His most cited work include:
- Histopathological Image Analysis: A Review (1080 citations)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. (982 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Histology are his primary areas of study.
His work in Deep learning, Convolutional neural network, Digital pathology, Image and Pixel is related to Artificial intelligence.
His Deep learning study incorporates themes from Domain and Object.
His Pattern recognition research is multidisciplinary, relying on both Breast cancer and Cluster analysis.
Nasir M. Rajpoot interconnects Grading, Algorithm and Histopathology in the investigation of issues within Breast cancer.
His Histology study integrates concerns from other disciplines, such as Cancer, Colorectal cancer, Artificial neural network, H&E stain and Stain.
He most often published in these fields:
- Artificial intelligence (101.81%)
- Pattern recognition (71.60%)
- Computer vision (26.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (101.81%)
- Pattern recognition (71.60%)
- Deep learning (28.40%)
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 Histology.
Nasir M. Rajpoot has researched Artificial intelligence in several fields, including Machine learning and Cancer Histology.
Nasir M. Rajpoot combines subjects such as Artificial neural network and Computational pathology with his study of Pattern recognition.
His work investigates the relationship between Deep learning and topics such as Domain that intersect with problems in RGB color model, Medical imaging, Contextual image classification and Task.
Nasir M. Rajpoot works mostly in the field of Segmentation, limiting it down to topics relating to Digital pathology and, in certain cases, Concordance and Cellular pathology.
His biological study deals with issues like Colorectal cancer, which deal with fields such as Algorithm, Tumor-infiltrating lymphocytes and H&E stain.
Between 2019 and 2021, his most popular works were:
- A Multi-Organ Nucleus Segmentation Challenge (40 citations)
- Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images (19 citations)
- Cellular community detection for tissue phenotyping in colorectal cancer histology images (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Segmentation, Histology and Deep learning.
His work deals with themes such as Head and neck cancer and Epithelial dysplasia, which intersect with Artificial intelligence.
His Pattern recognition research focuses on Convolutional neural network in particular.
The Segmentation study combines topics in areas such as Domain and Medical imaging.
His Deep learning study integrates concerns from other disciplines, such as RGB color model and Object.
The concepts of his Pixel study are interwoven with issues in Cancer and Feature extraction.
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