Zhi Lu

Marc Combalia, Veronica Vilaplana

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.

Zhi Lu, Gustavo Carneiro, Neeraj Dhungel et al.

In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($μ$C). Currently, the most effective methods are based on three steps: 1) detection of individual $μ$C candidates, 2) clustering of individual $μ$C candidates, and 3) classification of $μ$C clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends on a relatively large number of $μ$C detections within a certain area. In this paper, we propose a novel approach to $μ$C detection, consisting of the detection \emph{and} classification of individual $μ$C candidates, using shape and appearance features, using a cascade of boosting classifiers. The final step in our approach then clusters the remaining individual $μ$C candidates. The main advantage of this approach lies in its ability to reject a significant number of false positive $μ$C candidates compared to previously proposed methods. Specifically, on the INbreast dataset, we show that our approach has a true positive rate (TPR) for individual $μ$Cs of 40\% at one false positive per image (FPI) and a TPR of 80\% at 10 FPI. These results are significantly more accurate than the current state of the art, which has a TPR of less than 1\% at one FPI and a TPR of 10\% at 10 FPI. Our results are competitive with the state of the art at the subsequent stage of detecting clusters of $μ$Cs.