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Multi-Label Learning with Label Enhancement
The task of multi-label learning is to predict a set of relevant labels for
the unseen instance. Traditional multi-label learning algorithms treat each
class label as a logical indicator of whether the corresponding label is
relevant or irrelevant to the instance, i.e., +1 represents relevant to the
instance and -1 represents irrelevant to the instance. Such label represented
by -1 or +1 is called logical label. Logical label cannot reflect different
label importance. However, for real-world multi-label learning problems, the
importance of each possible label is generally different. For the real
applications, it is difficult to obtain the label importance information
directly. Thus we need a method to reconstruct the essential label importance
from the logical multilabel data. To solve this problem, we assume that each
multi-label instance is described by a vector of latent real-valued labels,
which can reflect the importance of the corresponding labels. Such label is
called numerical label. The process of reconstructing the numerical labels from
the logical multi-label data via utilizing the logical label information and
the topological structure in the feature space is called Label Enhancement. In
this paper, we propose a novel multi-label learning framework called LEMLL,
i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
numerical labels and label enhancement into a unified framework. Extensive
comparative studies validate that the performance of multi-label learning can
be improved significantly with label enhancement and LEMLL can effectively
reconstruct latent label importance information from logical multi-label data.Comment: ICDM 201
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations.Comment: MICCAI 2017 Camera Read
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