• Publications
  • Influence
The use of the area under the ROC curve in the evaluation of machine learning algorithms
  • A. Bradley
  • Mathematics, Computer Science
  • Pattern Recognit.
  • 1 July 1997
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluateExpand
  • 3,955
  • 396
Perceptual quality metrics applied to still image compression
We present a review of perceptual image quality metrics and their application to still image compression. The review describes how image quality metrics can be used to guide an image compressionExpand
  • 362
  • 20
An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells
In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree ofExpand
  • 113
  • 19
A wavelet visible difference predictor
  • A. Bradley
  • Mathematics, Medicine
  • IEEE Trans. Image Process.
  • 1 May 1999
In this paper, we describe a model of the human visual system (HVS) based on the wavelet transform. This model is largely based on a previously proposed model, but has a number of modifications thatExpand
  • 168
  • 15
Rule extraction from support vector machines: A review
Over the last decade, support vector machine classifiers (SVMs) have demonstrated superior generalization performance to many other classification techniques in a variety of application areas.Expand
  • 150
  • 11
Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells
In this paper we describe an algorithm for accurately segmenting the individual cytoplasm and nuclei from a clump of overlapping cervical cells. Current methods cannot undertake such a completeExpand
  • 74
  • 10
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
HighlightsWe introduce a novel automated CAD system with minimal user intervention that can detect, segment and classify breast masses from mammograms. We explore deep learning and structured outputExpand
  • 113
  • 9
Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models
We show two important findings on the use of deep convolutional neural networks (CNN) in medical image analysis. First, we show that CNN models that are pre-trained using computer vision databasesExpand
  • 148
  • 8
Shift-invariance in the Discrete Wavelet Transform
In this paper we review a number of approaches to reducing, or removing, the problem of shift variance in the discrete wavelet transform (DWT). We describe a generalization of the critically sampledExpand
  • 114
  • 8
Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms
In this paper, we explore the use of deep convolution and deep belief networks as potential functions in structured prediction models for the segmentation of breast masses from mammograms. InExpand
  • 81
  • 7