2010 - Fellow of the Royal Academy of Engineering (UK)
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Andrew P. Bradley.
Pattern Recognition (1997)
Perceptual quality metrics applied to still image compression
Michael P. Eckert;Andrew P. Bradley.
Signal Processing (1998)
Intelligible support vector machines for diagnosis of diabetes mellitus
Nahla Barakat;Andrew Bradley;Mohamed Barakat.
IEEE Journal of Biomedical and Health Informatics (2010)
Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus
Nahla Barakat;Andrew P Bradley;Mohamed Nabil H Barakat.
international conference of the ieee engineering in medicine and biology society (2010)
A wavelet visible difference predictor
IEEE Transactions on Image Processing (1999)
Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models
Gustavo Carneiro;Jacinto C. Nascimento;Andrew P. Bradley.
medical image computing and computer assisted intervention (2015)
A deep learning approach for the analysis of masses in mammograms with minimal user intervention.
Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley.
Medical Image Analysis (2017)
Rule extraction from support vector machines: A review
Nahla Barakat;Andrew P. Bradley.
Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests
Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley.
digital image computing techniques and applications (2015)
An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells
Zhi Lu;Gustavo Carneiro;Andrew P. Bradley.
IEEE Transactions on Image Processing (2015)
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