What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Andrew P. Bradley mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Segmentation.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His work in the fields of Machine learning, such as Cross-validation, overlaps with other areas such as Type 2 diabetes.
His Deep learning study combines topics in areas such as Convolutional neural network and Medical imaging.
His work in Artificial neural network addresses issues such as Support vector machine, which are connected to fields such as Feature extraction, Generalization, Knowledge extraction and Information extraction.
The concepts of his Classifier study are interwoven with issues in Analysis of variance, Standard error, Perceptron and Receiver operating characteristic.
His most cited work include:
- The use of the area under the ROC curve in the evaluation of machine learning algorithms (3942 citations)
- Perceptual quality metrics applied to still image compression (315 citations)
- Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus (193 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Audiology and Machine learning.
Segmentation, Deep learning, Image segmentation, Classifier and Feature extraction are the subjects of his Artificial intelligence studies.
He has researched Pattern recognition in several fields, including Visualization, Breast MRI, Data set and Receiver operating characteristic.
His research in Computer vision intersects with topics in Algorithm and Virtual microscopy.
His Algorithm research is multidisciplinary, incorporating elements of Discrete wavelet transform, Wavelet and Image scaling.
As a member of one scientific family, Andrew P. Bradley mostly works in the field of Audiology, focusing on Stimulus and, on occasion, Speech recognition.
He most often published in these fields:
- Artificial intelligence (49.56%)
- Pattern recognition (25.66%)
- Computer vision (19.91%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (49.56%)
- Pattern recognition (25.66%)
- Deep learning (11.50%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Mammography.
He combines subjects such as Machine learning and Computer vision with his study of Artificial intelligence.
His Machine learning research incorporates elements of Training set and Baseline.
His research integrates issues of Lesion, Lesion detection, Breast MRI and Reinforcement learning in his study of Pattern recognition.
His work carried out in the field of Deep learning brings together such families of science as Natural language processing, Multimedia, Task, Image and Visualization.
His Segmentation research includes themes of Similarity, Random forest, Grayscale, Breast cancer and Feature extraction.
Between 2015 and 2021, his most popular works were:
- A deep learning approach for the analysis of masses in mammograms with minimal user intervention. (143 citations)
- Why rankings of biomedical image analysis competitions should be interpreted with care (87 citations)
- Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. (74 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Andrew P. Bradley spends much of his time researching Artificial intelligence, Deep learning, Pattern recognition, Segmentation and Classifier.
The study incorporates disciplines such as Machine learning, Mammography and Computer vision in addition to Artificial intelligence.
His biological study spans a wide range of topics, including Image and Training set.
His Pattern recognition research includes elements of Lesion, Magnetic resonance imaging, Breast lesion and Reinforcement learning.
Andrew P. Bradley interconnects Transfer of learning, Breast cancer and Computer-aided diagnosis in the investigation of issues within Segmentation.
His work on Classifier is being expanded to include thematically relevant topics such as Test set.
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