What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Cancer
His primary areas of study are Artificial intelligence, Machine learning, Contextual image classification, Object and Cognitive neuroscience of visual object recognition.
His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition.
His Pattern recognition study combines topics in areas such as Deep belief network and Representation.
His Machine learning study incorporates themes from Classifier and Feature extraction.
He interconnects Object detection, Pascal, Categorical variable and Benchmark in the investigation of issues within Cognitive neuroscience of visual object recognition.
He combines subjects such as Data science and Pattern recognition with his study of Categorical variable.
His most cited work include:
- ImageNet Large Scale Visual Recognition Challenge (18266 citations)
- Learning Deep Features for Discriminative Localization (3220 citations)
- 3D ShapeNets: A deep representation for volumetric shapes (2166 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Object and Machine learning.
His study in Image, Object detection, Deep learning, Cognitive neuroscience of visual object recognition and Discriminative model falls under the purview of Artificial intelligence.
His Cognitive neuroscience of visual object recognition research is multidisciplinary, relying on both Contextual image classification and Deep belief network.
Object and Convolutional neural network are frequently intertwined in his study.
The concepts of his Machine learning study are interwoven with issues in Feature extraction, Prior probability and Benchmark.
His Benchmark study combines topics from a wide range of disciplines, such as Categorical variable and Salience.
He most often published in these fields:
- Artificial intelligence (75.38%)
- Computer vision (32.31%)
- Pattern recognition (23.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Pathology (6.15%)
- Cancer (4.62%)
- Homologous Recombination Deficiency (3.08%)
In recent papers he was focusing on the following fields of study:
Aditya Khosla mostly deals with Pathology, Cancer, Homologous Recombination Deficiency, Cancer research and Histopathology.
His Pathology research is multidisciplinary, incorporating elements of Pixel, Image and Statistical model.
His biological study spans a wide range of topics, including Advanced breast, Multiple tumors and Trastuzumab.
Phases of clinical research and Paclitaxel is closely connected to Oncology in his research, which is encompassed under the umbrella topic of Advanced breast.
His Interpretability study is associated with Artificial intelligence.
His Artificial intelligence research integrates issues from Steatosis, Liver histology, Cirrhosis, Pathological staging and Disease.
Between 2018 and 2021, his most popular works were:
- The Combined Effect of FGFR Inhibition and PD-1 Blockade Promotes Tumor-Intrinsic Induction of Antitumor Immunity. (31 citations)
- Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer (3 citations)
- Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Cancer
His scientific interests lie mostly in Cancer, Phenotype, Pathology, Tumor microenvironment and Cancer research.
Aditya Khosla has included themes like Interpretability, Deep learning and Artificial intelligence in his Cancer study.
Multiple tumors, Protein expression, High resolution and Histopathology are fields of study that overlap with his Phenotype research.
His research on Cancer research frequently connects to adjacent areas such as Targeted therapy.
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