作者
Qiang Zhang, Abhir Bhalerao, Charles Hutchinson
发表日期
2017/6/25
研讨会论文
International Conference on Information Processing in Medical Imaging
页码范围
210-222
出版商
Springer, Cham
简介
We propose a learning method to identify which specific regions and features of images contribute to a certain classification. In the medical imaging context, they can be the evidence regions where the abnormalities are most likely to appear, and the discriminative features of these regions supporting the pathology classification. The learning is weakly-supervised requiring only the pathological labels and no other prior knowledge. The method can also be applied to learn the salient description of an anatomy discriminative from its background, in order to localise the anatomy before a classification step. We formulate evidence pinpointing as a sparse descriptor learning problem. Because of the large computational complexity, the objective function is composed in a stochastic way and is optimised by the Regularised Dual Averaging algorithm. We demonstrate that the learnt feature descriptors contain more …
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Q Zhang, A Bhalerao, C Hutchinson - International Conference on Information Processing in …, 2017